Economic Analysis of Technology Infrastructure 

Needs for Advanced Manufacturing 

Advanced Robotics and Automation

August 2016

Prepared for—

Economic Analysis Office

National Institute of Standards and 

Technology

100 Bureau Drive

Gaithersburg, MD 20899

This publication is available free of charge from:

http://dx.doi.org/10.6028/NIST.GCR.16-005

Prepared by—

Albert N. Link

Zachary T. Oliver

Alan C. O’Connor

RTI International

3040 E. Cornwallis Road

Research Triangle Park, NC 27709

NIST GCR 16-005
Acknowledgements

Many contributed to the preparation of this report, and we wish to acknowledge in particular the 

private companies, industry associations, research institutes, universities, and government 

agencies whose experts contributed data and insights underlying the analysis herein.

In addition to this study’s lead authors, significant contributors included Luca Alessandro 

Remotti of the Joint Institute for Innovation Policy (formerly of Intrasoft S.A.), Raimund 

Broechler of Intrasoft S.A., and Jeff Cope of RTI’s Innovation Advisors.

We would also like to acknowledge the advice and subject matter expertise contributed by NIST 

economic and scientific staff. Specifically, we are especially grateful for the contributions of

 Gary Anderson of NIST’s Economic Analysis Office, 

 Timothy Burns of NIST’s Applied and Computational Mathematics Division,

 Stephen Campbell of NIST’s Economic Analysis Office,

 Heather Evan of NIST’s Program Coordination Office,

 Simon Frechette of NIST’s Systems Integration Division, 

 Albert Jones of NIST’s Systems Integration Division,

 James Liddle of NIST’s Center for Nanoscale Science and Technology,

 Eric Lin of NIST’s Materials Science and Engineering Division, 

 Richard Ricker of NIST’s Materials Measurement Science Division,

 Christopher Soles of NIST’s Materials Science and Engineering Division,

 Albert Wavering of NIST’s Intelligent Systems Division, and

 Paul Zielinski of NIST’s Technology Partnerships Office.

Disclaimer

This publication was produced as part of contract GS10F0097L with the National Institute of 

Standards and Technology. The contents of this publication do not necessarily reflect the views 

or policies of the National Institute of Standards and Technology or the US Government.

About RTI

RTI International is one of the world’s leading research institutes, dedicated to improving the 

human condition by turning knowledge into practice. Our staff of more than 4,150 provides 

research and technical services to governments and businesses in more than 75 countries in the 

areas of health and pharmaceuticals, education and training, surveys and statistics, advanced 

technology, international development, economic and social policy, energy and the environment, 

and laboratory testing and chemical analysis. For more information, visit www.rti.org.

RTI International is a registered trademark and a trade name of Research Triangle Institute.
NIST GCR 16-005

Economic Analysis of Technology 

Infrastructure Needs for Advanced 

Manufacturing: Advanced Robotics and 

Automation

Prepared for

U.S. Department of Commerce

Economic Analysis Office

Office of the Director

National Institute of Standards and Technology

Gaithersburg, MD 20899

By

Albert N. Link

Zachary T. Oliver

Alan C. O’Connor

RTI International

This publication is available free of charge from:

http://dx.doi.org/10.6028/NIST.GCR.16-005

August 2016

U.S. Department of Commerce 

Penny Pritzker, Secretary

National Institute of Standards and Technology 

Willie May, Under Secretary of Commerce for Standards and Technology and Director 
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Contents

Section Page

Executive Summary ES-1

1 Overview of Robotics and Automation1-1

1.1 Definition of Technology Infrastructure.......................1-4

1.1.1 Infratechnologies...........................................1-6

1.1.2 Technology Platforms .....................................1-6

1.2 Proprietary Technologies ..........................................1-7

1.3 Scope of the Analysis ...............................................1-7

1.4 Barriers to Development and Adoption .......................1-8

1.5 Needed Capabilities and Technology Infrastructure ....1-11

1.6 What Distinguishes This Report ...............................1-13

2 Analysis Methods and Data Collection2-1

2.1 Data Collection........................................................2-1

2.1.1 Selection of Relevant Sectors ..........................2-1

2.1.2 Interviews ....................................................2-1

2.1.3 Developers versus End Users ..........................2-2

2.2 Economic Models .....................................................2-3

2.2.1 Developers ...................................................2-4

2.2.2 End Users .....................................................2-4

2.3 Conservative Nature of the Economic Modeling 

Approach................................................................2-7

3 Industry Trends and Technology Gaps3-1

3.1 Trends in Robotics and Automation............................3-2

3.1.1 Robot Performance-to-Price Ratio ....................3-5

3.1.2 Traditional Industrial Robotics versus 

Collaborative Robotics ....................................3-6
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3.2 Industry Capabilities and Potential Benefits...............3-11

3.2.1 Safe Human-Robot Interaction ......................3-12

3.2.2 Sensing and Perception for Unstructured 

Environments..............................................3-17

3.2.3 Objective, Low-Cost Performance 

Characterization ..........................................3-19

3.2.4 Interoperability and Modularity......................3-20

3.2.5 Intuitive Interfaces ......................................3-23

3.2.6 Modeling and Simulation...............................3-23

3.3 The Importance of Infratechnologies to Support 

Robotics and Automation........................................3-25

3.4 Stakeholders in Robotics and Automation .................3-28

3.4.1 Observers ...................................................3-29

3.4.2 Developers .................................................3-30

3.4.3 System Integrators ......................................3-30

3.4.4 End Users ...................................................3-31

4 Quantitative Results and Economic Impact 

Analysis4-1

4.1 Firm-Level Developer Sales Impacts ..........................4-2

4.2 National-Level Developer Sales Impacts .....................4-3

4.2.1 Market Size...................................................4-3

4.2.2 Total National Impacts for Developers ..............4-4

4.2.3 Other Impacts Not Quantified..........................4-4

4.3 Firm-Level End User Impacts ....................................4-5

4.4 National-Level End user Impacts ...............................4-8

4.4.1 National-Level End User Impact by 

Capability ...................................................4-13

4.4.2 Other Impacts Not Quantified........................4-14

5 Safe Human-Robot Interaction5-1

5.1 Barriers to Innovation and Sources of market 

Failure....................................................................5-1

5.2 Technology Infrastructure Needs and Potential 

Roles for NIST.........................................................5-3

5.3.1 Risk Assessment and Measurement Tools .........5-3

5.3.2 Taxonomies and Paradigms for Human-

Robot Interaction...........................................5-4

5.3.3 Coordination with Other Groups Such as the 

Robotic Industries Association .........................5-5

5.3.4 Other Potential Roles......................................5-5
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6 Sensing and Perception for Unstructured 

Environments6-1

6.1 Barriers to Innovation and Sources of Market 

Failure....................................................................6-2

6.2 Technology Infrastructure Needs and Potential 

Roles for NIST.........................................................6-3

6.2.1 New Technologies for Sensing and 

Perception ....................................................6-3

7 Intuitive Interfaces7-1

7.1 Barriers to Innovation and Sources of Market 

Failure....................................................................7-2

7.2 Technology Infrastructure Needs and POtential 

Roles for NIST.........................................................7-3

7.2.1 Standardized Robot Programming....................7-3

7.2.2 Other Potential Roles......................................7-4

8 Interoperability and Modularity8-1

8.1 Barriers To Innovation and Sources of Market 

Failure....................................................................8-1

8.2 Technology Infrastructure Needs and Potential 

Roles for NIST.........................................................8-2

8.2.1 Interoperability with Components and 

Peripherals (Horizontal Interoperability) ...........8-2

8.2.2 Interoperability with Other Systems 

(Vertical Interoperability)................................8-3

8.2.3 Standards.....................................................8-3

9 Modeling and Simulation9-1

9.1 Barriers To Innovation and Sources of Market 

Failure....................................................................9-1

9.2 Technology Infrastructure Needs and Potential 

Roles for NIST.........................................................9-2

10 Objective, Low-Cost Performance Characterization 10-1

10.1 Barriers to Innovation and Sources of Market 

Failure..................................................................10-1

10.2 Technology Infrastructure Needs and Potential 

Roles for NIST.......................................................10-2

11 Conclusion 11-1

11.1 Capabilities and Infratechnology Needs ....................11-2

11.2 Economic Impacts .................................................11-3

11.3 Technology Infrastructure Needs and Potential 

Roles for NIST.......................................................11-4
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11.3.1 Risk Assessment and Measurement Tools for 

Safe Human-Robot Interaction ......................11-4

11.3.2 New Technologies for Sensing and 

Perception ..................................................11-5

11.3.3 Standardized, More Intuitive Robot 

Programming ..............................................11-5

11.3.4 Interoperability ...........................................11-6

11.3.5 Coordination with Other Organizations ...........11-6

Bibliography Biblio-1

Appendixes

A: Interview Guide for Robotics/Automation Systems 

Developers .............................................................A-1

B: Interview Guide for Robotics/Automation Systems 

Users .....................................................................B-1

C: Importance Scores...................................................C-1

D: Vignette ................................................................ D-1
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Figures

Number Page 3-1. U.S. Stock of Robots and the Nominal Price per Robot, 

2004–2014.....................................................................3-2

3-2. Shipments of Robots to North American Customers, 

2004–2014.....................................................................3-4

3-3. Distribution of Robots in the United States, by Major 

Application Area (Based on 2012 U.S. Shipments)...............3-8

3-4. Distribution of Robots, by Industry (Based on 2012 U.S. 

Shipments) ....................................................................3-9

3-5. Importance of Capabilities/Needs and Corresponding 

Infratechnology Needs ...................................................3-26

3-6. Importance of Capabilities/Needs and Corresponding 

Infratechnology Needs, Diverging Stacked Bar Chart .........3-27

4-1. Percentage Change in Developer Sales Associated with 

Enhanced Infratechnology ................................................4-3

4-2. Range of Cost Impacts on Shop Floor Expenditures 

Associated with Enhanced Infratechnology, by Factor 

Input.............................................................................4-7

4-3. Range of Total Composite Cost Impact on Shop Floor 

Expenditures Associated with Enhanced Infratechnology ......4-8

4-4. KLEM Shares of National Factor Expenditure.......................4-9

4-5. KLEM Shares of National Factor Expenditure, by Industry 

Sector..........................................................................4-10

4-6. Total Cost Impact, by Capability (Millions of 2013 US$) .....4-14

11-1. Importance of Capabilities/Needs and Corresponding 

Infratechnology Needs, Diverging Stacked Bar Chart .........11-2
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Tables

Number Page 1-1. Definitions of Key Concepts ..............................................1-5 1-2. Barriers to Developing and Adopting New Technology 

That Bring about Market Failure ........................................1-9

1-3. Required Capabilities for the Application of Advanced 

Robotics in Manufacturing and Associated 

Infratechnology Needs ...................................................1-12

2-1. End User Industry Coverage .............................................2-5

3-1. Forecast CAGR in Industrial Robot Installations, 2016–

2018 .............................................................................3-4

3-2. Required Capabilities for the Application of Advanced 

Robotics in Manufacturing, Associated Infratechnology 

Needs, and Benefits.......................................................3-11

3-3. Number of interviewees by Position in the Value Chain ......3-29

4-1. Percentage Change in Factor Inputs Due to Having 

Industry Needs Met .........................................................4-5

4-2. Stylized Example of Scaling Sample Impacts to the 

National Level, End User ................................................4-11

4-3. Economic Impact Summary Table ...................................4-12

5-1. Technology Infrastructure Needs Related to Safe HRI ..........5-4

6-1. Technology Infrastructure Needs Related to Sensing and 

Perception for Unstructured (or Less-Structured) 

Environments .................................................................6-3
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5

7-1. Technology Infrastructure Needs Related to Intuitive 

Interfaces ......................................................................7-3

8-1. Technology Infrastructure Needs Related to 

Interoperability and Modularity .........................................8-3

9-1. Technology Infrastructure Needs Related to Modeling 

and Simulation................................................................9-3

11-1. Percentage Change in Factor Inputs Due to Meeting 

Industry Needs .............................................................11-3

11-2. Economic Impact Summary Table ...................................11-4
ES

-1

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Executive Summary

Advanced robotics and automation have been discussed as 

potential game-changing technologies for strengthening the 

U.S. manufacturing sector, particularly for small and medium-

sized manufacturers (SMEs).Advanced robotics can help to 

decrease production costs as well as offer greater flexibility to 

manufacturers to respond to changing market conditions and 

consumer preferences. Next-generation robots could be mobile 

and autonomous in their environment, with the ability to 

operate in unstructured environments free from the physical 

cages that have surrounded traditional industrial robots for 

decades and to collaborate safely with humans while doing so.

1

Many companies face barriers in adopting robotics technologies

because of robotics’ hard and soft costs and the associated 

learning curve. Missing from the marketplace are standards, 

technology platforms, and other fundamental pieces of 

technology, making technology adoption more costly and 

difficult than it needs to be. Because firms cannot profit from 

developing these public-good technologies, they do not develop 

them, which in turn discourages innovation and undermines 

American competitiveness. Public-sector support of relevant 

technology infrastructure will help lessen, if not outright,

remove many barriers.

Economic benefits associated with meeting technology 

infrastructure needs in robotics and automation could approach 

$40 billion per year. This represents, on average, a 5% 

reduction in the shop floor cost of production. This economic 

 1 Many of these concepts were outlined in A Roadmap for U.S. 

Robotics—From Internet to Robotics (Robotics-VO, 2009), which led 

to the National Robotics Initiative, and the successor report 

(Robotics-VO, 2013).
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: Advanced 

Robotics and Automation

ES-2

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5

benefit is largely driven by material and labor savings, which 

are partially offset by an increase in capital investment.

We consider this estimate conservative because it does not 

capture several hard–to-measure benefits, such as improved 

product quality and accelerated market transformation. It is 

also based on recent (and not projected) industry data.

ES.1 SCOPE OF THE ANALYSIS

This study is a collaborative effort among multiple units within 

the National Institute of Standards and Technology (NIST) to 

determine technology infrastructure needs to support advanced 

manufacturing. It presents findings of an economic analysis of 

the technology infrastructure, which includes standards, 

measurement, and general-purpose technology, and the role of 

this infrastructure in the efficient development and adoption of 

advanced robotics and automation in the United States.

There is currently a lack of understanding of what is inhibiting 

the development and adoption of robotics technology. If 

barriers to adoption do exist, the question is What role can 

government play in mitigating these barriers and accelerating 

the penetration of robotics technology in the U.S. 

manufacturing sector? By addressing this question, this study 

represents a departure from most existing studies of robotics 

because it specifically focuses on needed technology 

infrastructure, which, because of its public good characteristics, 

is in the purview of NIST.

The objectives of this strategic planning study were to

 identify current and emerging trends related to robotics 

and automation, with specific focus on applications in 

manufacturing;

 identify technology infrastructure needs to support the 

development and adoption of robotics and automation 

technology;

 document the challenges and barriers that inhibit the 

development of technology infrastructure;

 estimate the economic benefit of meeting these 

technology infrastructure needs; and

 assess potential roles for NIST in meeting technology 

infrastructure needs.
Executive Summary

ES-3

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Ultimately, the purpose of the study is to provide NIST with 

information on industries’ technology infrastructure needs to 

help inform NIST’s strategic planning.

ES.2 ANALYSIS APPROACH

The methodology includes the collection and analysis of 

qualitative and quantitative data from primary and secondary 

sources. To ensure that a variety of perspectives are accounted 

for, RTI interviewed a cross-section of 84 experts from industry 

associations and research centers, developers of robotic 

systems and component technologies, system integrators, and 

end users of robotics within the manufacturing sector. We also 

had informal conversations with individuals at conferences and 

industry events, which contributed to the findings in this report.

We approached interviewees with a set of capabilities, which, 

through our first phase of interviews

2, we found to be of the 

utmost importance to members of the manufacturing value 

chain:  Safe human

-robot interaction (HRI)

 Sensing and perception for unstructured (or less-

structured) environments

 Objective, low-cost performance characterization

 Interoperability and modularity  Intuitive interfaces  Modeling and simulation

These six capabilities were consistently identified by 

manufacturers as needed for the full and efficient application of 

advanced robotics and automation technology.

Quantitative information from interviews about the impact that 

these capabilities and the underlying technology infrastructure 

would have formed the basis of our economic models that 

estimate the economic benefits that enhanced technology 

infrastructure would have on the U.S. manufacturing sector. 

 2 Phase 1 interviews occurred in 2014. Interviews lasted between 30 

and 60 minutes and were conducted by telephone. The interviews 

were unstructured to offer the greatest flexibility in eliciting insights 

about the current status and future opportunities of technologies 

and applications. In-depth interviews were particularly effective for 

data collection given the nuanced topics of interest.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: Advanced 

Robotics and Automation

ES-4

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5

Other key parameters in our models were derived from publicly 

available data on the manufacturing sector.

ES.3 ANALYSIS OF TECHNOLOGY 

INFRASTRUCTURE NEEDS

Table ES-1 summarizes the technology infrastructure needed to 

support robotics and automation capabilities along with the 

associated potential benefits and impacts resulting from an 

enhanced technology infrastructure. These benefits underpin 

the economic impacts estimated as part of the study.

Table ES-1. Required Capabilities for the Application of Advanced Robotics in 

Manufacturing, Associated Infratechnology Needs, and Benefits

Industry Capabilities

Examples of Infratechnology to 

Help Meet Needs

Potential Benefits and 

Impacts

Safe human-robot interaction 

(HRI)

Universal standards for developers of 

robotics technologies and the 

application of these technologies in 

manufacturing settings with robots 

working in close proximity to people 

(see more below on 

sensing/perception for unstructured 

environments, relevant for intuitive 

HRI)

• Test protocols, objective 

scientific and engineering data, 

reference databases, and other 

technical inputs into standards 

for safe HRI (power/forcelimiting, speed/separation 

monitoring, hand-guided 

operation, safety-rated 

monitored stop)

• More flexible, smallerfootprint production lines

• New and creative use cases of 

robots working in close 

proximity and in collaboration 

with people

• Lower integration costs

• Improved safety

• Reduced market risk for 

developers

• Reduced liability for end users

• Increased adoption of 

collaborative robots

Sensing and perception for 

unstructured (or less-structured) 

environments

Improved perception (and the ability 

to plan and re-plan the robot’s 

actions based on what it “sees” and 

“knows”) gives a robot greater 

autonomy, lessening its demand that 

its work environment meet stringent 

tolerances

• Sensor registration and 

calibration

• Performance characterization 

(benchmarks, testbeds, and 

technical inputs to standards to 

characterize the performance 

of systems, subsystems, and 

components)

• Sensing/perception 

engines/architectures

• Proof-of-concept robotics 

applications of knowledge 

representation and reasoning

• Lower integration costs 

associated with 

accommodating tolerances

• Flexible navigation of 

unstructured or lessstructured environments

• More flexible plant layouts

• Improved safety

• Optimized robot motions

• Data streams to calibrate 

simulation models

Objective, low-cost performance 

characterization

Making it easier for robotics users to 

know what they are buying and for 

developers and suppliers to show 

what their systems do

• Common performance metrics, 

objective data, testbeds, test 

methods, and benchmarks to 

characterize the performance 

attributes of advanced 

systems, subsystems, and 

components

• Reduced uncertainty

• Improved understanding of 

new technologies

• Increased adoption of robotics 

by SMEs

(continued)
Executive Summary

ES-5

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5

Table ES-1. Required Capabilities for the Application of Advanced Robotics in 

Manufacturing, Associated Infratechnology Needs, and Benefits (continued)

Industry Capabilities Examples of Infratechnology to 

Help Meet Needs

Potential Benefits and 

Impacts

Interoperability and modularity

Plug-and-play for system 

components, enabled by standards 

for physical and electronic interfaces 

and software interfaces or 

translators

• Objective technical inputs into 

the standard-setting process: 

scientific and engineering data, 

benchmarks, testbeds, 

objective third-party testing of 

candidate technologies and 

configurations

• Plug-and-play functionality

• Reduced integration costs 

(physical and software 

interfaces)

• Modular development of 

systems

• Increased adaptability of 

robotic systems

• Scalable, reconfigurable, and 

reusable robotic systems

• Reduced retooling costs

• Increased adoption in 

industries with small 

production runs

Intuitive interfaces

Enabling rapid programming and 

training without specialized skills

• Protocols to simplify the 

programming, training, and 

rapid re-tasking of robots

• Standard programming 

language for industrial robotics 

analogous to SQL or HTML

• Simplified programming

• Reduced setup time and setup 

costs

• Enables individuals without 

specialized training to 

commission a robotic system

Modeling and simulation

Virtual factory floor allowing 

modeling and simulation, calibrated 

based on real-time data feed from 

robots, machine tools, sensors, and 

control systems on the floor

• Robust, open, real-time 

operating system on the 

factory floor

• Reference models, modeling 

frameworks to fully integrate 

robots into models of the 

manufacturing environment 

and enable robust 

simulation/prediction

• Control of processes from 

central dashboard

• Improved prediction

• Adjustments can be optimized

• Reduced delay and work 

stoppage

• Software reconfigurable 

factory floor

• Reduced retooling costs

• Improved “as-built” 

documentation

• Using robot teaching to refine 

simulation models

To provide a barometer for NIST in terms of how it might be 

able to accelerate the development and adoption of robotics 

technology most effectively, interviewees were asked to 

characterize the importance of each of the six capabilities in 

Table ES-1 and corresponding infratechnologies. Safe HRI, 

sensing and perception for unstructured environments, intuitive 

interfaces, and interoperability and modularity appear to be the 

most important needs, whereas the interviewees evaluated 

modeling and simulation and objective, low-cost performance 

characterization as noticeably less important.

Figure ES-1 summarizes the responses about the level of 

importance (measured as 1 to 5, where 1 represents the least 

important and 5 represents the most important) by capability.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: Advanced 

Robotics and Automation

ES-6

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5

Figure ES-1. Importance of Capabilities/Needs and Corresponding Infratechnology Needs

Note: The level of importance of each capability is measured on a scale from 1 to 5, where 1 represents the least 

important and 5 represents the most important.

The percentage of interviewees who responded with a 4 or 5 

are in darker shades, and the percentage of interviewees who 

responded with a 3 or below are in lighter shades. The average 

importance score is overlaid in the gray boxes.

As reflected in the figure, the majority of interviewees 

evaluated safe HRI, sensing and perception for unstructured 

environments, intuitive interfaces, and interoperability and 

modularity as being important.3 Modeling and simulation and 

objective, low-cost performance characterization are noticeably 

less important.

The following subsections discuss the needs and barriers to 

development within each capability area.

ES.3.1 Safe Human-Robot Interaction

Safe HRI is arguably the most important and potentially 

transformative of the six needed capabilities for robotics 

technology. The development and adoption of safe human-

 

3 That is, they evaluated the importance of the capability with a 4 or a 

5.
Executive Summary

ES-7

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robot technology is inhibited by several barriers to innovation 

(sources of market failure) such as the broad scope of 

commercial applications that is beyond the reach of any 

individual firm, technical risk, market risk due to safety issues 

and cultural acceptance, and difficulty in bringing together 

component technologies that are necessary for various 

approaches to safe HRI.

Enhanced infratechnologies needed to enable safe HRI include 

standardized risk assessment tools, test methods, and new 

taxonomies and paradigms of safe HRI. Another industry need 

mentioned was public sector coordination with major players 

that have influence on robot safety standards, among other 

things.

ES.3.2 Sensing and Perception for Unstructured Environments

Sensing and perception for unstructured environments is one of 

the most important capabilities because it would allow robots to 

navigate unstructured and/or semi-structured environments, 

which would enable new applications and more flexible 

deployment of robots in the factory environment. Improved 

sensing and perception would also directly support safe HRI.

The development and adoption of sensing and perception 

technology are inhibited by several barriers to innovation 

(sources of market failure) such as the difficulty in bringing 

component technologies together, the scope of commercial 

applications being broader than the market strategy of any one 

firm, and the long and uncertain lag between R&D investments 

and returns.

There appears to be a potential role for the public sector, 

perhaps through NIST, to play a role in enhancing 

infratechnologies by conducting research on new sensing 

technologies, improving interoperability in support of sensing 

and perception, and working on market demonstration efforts. 

RTI also offers the idea of closer coordination and collaboration 

with other organizations as a vehicle for lessening innovation 

barriers, based on many comments from interviewees.

ES.3.3 Intuitive Interfaces

Intuitive interfaces for interacting with robots is an important 

industry need, especially considering the lack of skilled workers 

and technical knowledge that many companies described as a 

barrier to adoption. Intuitive methods of programming and 
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: Advanced 

Robotics and Automation

ES-8

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5

teaching robots such as graphical user interfaces (GUIs) would 

make robots more accessible to all manufacturers, specifically 

SMEs, which tend to have a smaller pool of technical knowledge 

to tap into.

Comparisons were drawn between the need for intuitive robot 

interfaces and other languages such as SQL and M-code.4

These comparisons may indicate that although a GUI would be 

beneficial, the most important aspect of intuitive interfaces may 

simply be standardizing robot programming across the 

industry. This would prevent individuals from having to learn 

multiple robot programming languages and would allow a set of 

work instructions to be executed by any robot regardless of 

supplier.

The development and adoption of intuitive interfaces is 

inhibited by several barriers to innovation (sources of market 

failure) such as the risk that R&D outcomes, although 

technically sufficient, will gain insufficient market acceptance 

and cause difficulties in bringing together component 

technologies.

Industry initiatives toward this end, such as Robot Operating 

System (ROS) and ROS-Industrial (ROS-I), are under way. 

NIST is working with this effort and is using ROS-I in ongoing 

projects. Industry pointed out that there is value in NIST 

research activities that dovetail and complement existing 

efforts.

ES.3.4 Interoperability and Modularity

Interoperability and modularity can support other capabilities 

such as sensing and perception and intuitive interfaces. 

Improved interoperability and modularity of robotics technology 

alleviates the difficulty of bringing together component 

technologies, which is a common barrier to innovation in this 

industry. Plug-and-play interoperability can be achieved by 

standardizing physical interfaces, electronic interfaces, and 

software interfaces, or translators.

Interoperability and modularity is inhibited by several barriers 

to innovation (sources of market failure) such as positive 

network externalities, difficulties in bringing together 

 

4 M-code is a subset of machine functions in G-code, a commonly used 

numerical control programming language.
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component technologies such as physical interfaces for 

hardware or communications protocols for software, and 

industry structure.

Industry indicated that research-based standardization efforts 

would help accelerate the development and adoption of 

interoperable and modular technologies. Industry also indicated 

the importance of coordination among robot manufacturers, 

end users, industry associations, and professional groups, and 

the need for a neutral, third-party coordinator. Horizontal and 

vertical interoperability are needed.

ES.3.5 Modeling and Simulation

Modeling and simulation appeared to be somewhat less 

important relative to some of the other capabilities. However, 

modeling and simulation tools for risk assessments could 

enable the more efficient deployment of collaborative robots.

Simulation could significantly reduce the time and cost involved 

in changing between production lines if the factory floor was 

software reconfigurable. Other benefits of simulation include a 

better understanding of tool paths and documenting as-built 

products compared with their digital counterparts. Simulation 

models could also be used to convert robot training and 

teaching on the factory floor into transferable knowledge that 

can be applied to different situations in the future.

Modeling and simulation is inhibited by two barriers to 

innovation (sources of market failure): technical risk and the 

difficulty of bringing together component technologies.

The most important future application of simulation appears to 

be for supporting safe HRI through risk assessment and 

quantification of relevant dimensions of HRI. Key 

infratechnologies that are needed include libraries, reference 

data, and reference models. There are also some industry 

needs around measuring the fidelity of simulation models.

ES.3.6 Objective, Low-Cost Performance Characterization

Objective, low-cost performance characterization would address 

informational asymmetries between developers and end users 

of robots. The importance of this capability depends on the 

unique context and situations of each end user.

If objective, low-cost performance characterization were to be 

standardized, it could stimulate innovation by giving users a 
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common way to communicate needs to developers and by 

giving developers a common target for focusing R&D efforts. 

However, interviewees suggested that a public role may be 

limited here because industry would need to lead any 

measurement standards. Generally, industry has to be involved 

in developing standards that it will use, but the importance of 

industry involvement is particularly strong in this case because 

performance measures are directly used to market robotics 

technologies.

Perhaps the single greatest need for performance 

characterization would be in the area of measurement 

supporting safe HRI.

ES.4 SUMMARY OF ECONOMIC IMPACTS

The deployment of robots in the U.S. manufacturing sector to

date has largely been in automotive manufacturing. If critical 

enabling infratechnologies were in place, advanced capabilities, 

such as safe HRI and enhanced sensing and perception, would 

be possible. The existence of advanced capabilities (and 

associated infratechnologies), as outlined in this report, would 

stimulate industry investment in robotics technology and lead 

to the realization of an estimated $40.4 billion in net economic 

savings of U.S. manufacturers per year, based on recent 

industry data.

Table ES-2 shows the average percentage change in capital, 

labor, energy, and materials (KLEM) due to having technology 

infrastructure needs met. End users indicated that labor costs 

could be reduced by an average of 18%, and materials costs 

could be reduced by 8%. These cost savings could be achieved 

by a net increase in capital expenditures of 22%. The economic 

impact of the increased adoption of robotics and automation on 

energy consumption would be negligible.

Table ES-2. Percentage Change in Factor Inputs Due to Meeting Industry Technology 

Infrastructure Needs for Robotics and Automation 

Factor Input Mean Impact of Meeting Industry Needs 

K: Capital +22%

L: Labor −18%

E: Energy +1%

M: Materials −8%
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Table ES-3. Economic Impact Summary Table

Industry Data, 2013 Cost Impacts, Billions

Percentage 

Sales Impact

Shop Floor 

KLEM 

National 

Factor 

Expenditure

K: 

Capital

L: 

Labor

E: 

Energy

M: 

Materials Total

Total $2.13 

trillion $759 billion +$11.4 −$22.1 −$0.4 −$29.3 −$40.4 −5.3%

Changes in factor inputs were then scaled to the national level 

to estimate the economic impacts associated with enhanced 

infratechnology. Table ES-3 shows that national economic 

impacts are estimated to be approximately $40.4 billion.

The aggregate national economic impacts represent, on 

average, a 5% reduction in national factor expenditures. 

Materials savings, although a smaller percentage impact than 

labor savings, accounts for the largest share of savings because 

it accounts for approximately three-fifths of manufacturing 

production costs.

Figure ES-2 shows the distribution of impacts apportioned to 

each of the six capabilities. The distribution of impacts reflects 

the importance scores provided by end users. Intuitive 

interfaces, safe HRI, and sensing and perception all have 

impacts greater than $7 billion. These are followed by 

interoperability and modularity as well as modeling and 

simulation, with $6.6 billion and $6.3 billion in cost impacts, 

respectively. Reflecting its lower overall importance score, 

objective, low-cost performance characterization has the 

smallest impact of all capabilities with $5.4 billion.5

 

5 There are often strong complementarities or “interaction effects” 

between capabilities that we were not able to quantify.
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Figure ES-2. Total Cost Impact, by Capability (Millions of 2013 US$)

Note that the economic impact estimates presented are 

conservative in that they focus on reductions in manufacturers’ 

production cost that would result from meeting the identified 

technology infrastructure needs. Not included in the economic 

impact calculations is the economic value associated with 

reduced R&D costs, improved product attributes, increased 

sales, or accelerating the introduction of new products to the 

market.
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1

Overview of 

Robotics and 

Automation

Advanced robotics and automation have been discussed as 

potential game-changing technologies for strengthening the 

U.S. manufacturing sector, particularly for small and medium-

sized manufacturers (hereafter, small and medium-sized 

enterprises[SMEs]). Increased development and adoption of 

this technology could also be an important competitive 

advantage for the United States, enabling U.S. manufacturers 

to maintain and improve international competitiveness through 

enhanced labor productivity, production efficiency, and quality. 

Furthermore, advanced robotics can offer greater flexibility to 

manufacturers to respond to changing market conditions and 

consumer preferences.

In calling for an Advanced Manufacturing Initiative, the 

President’s Council of Advisors on Science and Technology 

(PCAST) highlighted advanced robotics as a technology area 

where public-private investment could support advances in 

manufacturing (PCAST, 2011, p. 28).

6

 6 In its July 2012 report, PCAST called for increased research and 

development funding in 11 cross-cutting technology areas, one of 

which was industrial robotics (PCAST, 2012). In its 2013 annual 

report, the President’s Council of Economic Advisers emphasized 

again the importance of robotics technology: “The Administration 

also has proposed initiatives to replenish the technology pipeline, 

by increasing funding for advanced manufacturing R&D. Despite 

tightening budgets, the Administration has emphasized the 

importance of funding industrially relevant, advanced 

manufacturing technologies such as advanced materials, smart 

manufacturing, and robotics”(Council of Economic Advisers, 2013, 

p. 234).
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Part of the president’s Advanced Manufacturing Initiative was to 

establish the National Robotics Initiative, whose goal is to 

accelerate the development and use of robots in the United 

States.7

Next-generation robots could be mobile and autonomous in 

their environment, with the ability to operate in unstructured 

and semi-structured environments and to collaborate safely 

with humans while doing so. These next-generation robots 

could achieve all of these things without the restrictive and 

space-inefficient physical cages that have surrounded 

traditional industrial robots—the large, powerful, fast-moving 

robots that operate in safe, guarded space, cordoned off from 

factory workers—for decades.8 In fact, one of the objectives of 

the National Robotics Initiative is “the realization of such corobots working in symbiotic relationships with human partners”

(NSF, n.d.). Furthermore, these objectives could be achieved 

without using esoteric programming languages that require 

PhD-level training. Ultimately, next-generation robots will be 

easier to integrate into manufacturing production lines.

Some contend that “a dramatic takeoff in advanced robots is 

imminent. ... [G]rowth in the installed base of robotics will 

accelerate to around 10 percent annually during the next 

decade” (Sirkin, Zinser, & Rose, 2015, p. 6).

Experts interviewed for this study indicated that the 

collaborative robotics market is expected to grow substantially 

within the next few years. One forecast was that the market for 

collaborative robots will grow in excess of 100% each year for 

the next few years, regardless of the National Institute of 

Standards and Technology’s (NIST’s) actions, although 

enhanced technology infrastructure—the broad base of quasipublic technologies and technical knowledge that supports 

firms’, universities’, and laboratories’ research and 

 

7 Agencies involved in this initiative include the National Science 

Foundation (NSF), the National Aeronautics and Space 

Administration, the National Institutes of Health, the U.S. 

Department of Agriculture, and the U.S. Department of Defense.

See NSF (n.d.). Refer to 

https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503641 for 

additional details. 8 Many of these concepts were outlined in A Roadmap for U.S. 

Robotics—From Internet to Robotics (Robotics-VO, 2009), which 

helped lead to the National Robotics Initiative, and the successor 

report in 2013.
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development (R&D) and production of improved and entirely 

new products and processes—could have a multiplier effect on 

economic growth.

Another interviewee echoed a common theme that the growth 

of collaborative robotics will have an impact on for SMEs: “All of 

a sudden every mom and pop manufacturer can look at robotics 

and have it not be a stretch... The impact of less expensive, 

more flexible robots will be seen especially among small-and 

medium-sized fabricators. Each fabricator may only adopt 1 or 

2 robots at a time but there are hundreds of thousands of 

[fabricators].”

Robots are an important component of more flexible 

manufacturing systems. Intelligent automation could build 

robots’ capabilities to increase autonomy and flexibility to 

enable manufacturers to respond efficiently to customers’ 

needs and desires. To stay competitive in today’s marketplace, 

manufacturers must maintain flexibility in their production 

systems because products have shorter life cycles and 

consumers are demanding a greater variety of goods

(PricewaterhouseCoopers, 2014, p. 1).

Major advances could provide broad-based innovations

benefiting multiple industries, such as those provided by 

computer-aided design and computer-aided manufacturing, 

total quality management, and just-in-time manufacturing. 

However, advances in developing and commercializing robotics

technologies are happening more slowly in the United States

than in other industrialized nations such as the European Union 

(EU), Japan, China, and South Korea because of an 

underinvestment by private companies in robotics R&D. In fact, 

some claim that the underinvestment is not specific to the 

private sector; despite a handful of U.S. initiatives supporting 

robotics, the public sector has also underinvested in relevant 

innovations specifically compared with EU initiatives(MIT 

Committee to Evaluate the Innovation Deficit, 2015).

Furthermore, public-private investments in advancing robotics 

technology could help U.S. manufacturers compete in the global 

economy by enabling reductions in production costs, 

improvements in labor productivity, improvements in product 

quality, and reductions in time to market. These improvements 

would support a national policy imperative of reinvigorating the 

U.S. manufacturing sector.
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Given that the market for robotics and automation is 

characterized by several sources of market failure, the public 

sector can help the market achieve a more socially efficient use 

of its resources. This report, prepared for NIST’s Economic 

Analysis Office, explores technology infrastructure needs that 

NIST can help meet to support and enhance the development 

and adoption of robotics and automation in the U.S. 

manufacturing sector.

1.1 DEFINITION OF TECHNOLOGY

INFRASTRUCTURE

Technology infrastructure is the broad base of public and quasipublic technologies9 and technical knowledge that support the 

R&D and production efforts of firms, universities, and 

laboratories, as well as the development and adoption of 

improved and entirely new products, processes, and services 

(e.g., higher quality, more effective, more efficient, more 

productive).

Technology infrastructure supports and accelerates 

enhancements in advanced manufacturing capabilities. 

Specifically for robotics and automation, enhanced technology 

infrastructure could have the ability to enable capabilities such 

as

 safe human-robot interaction (HRI);

 sensing and perception for unstructured (or lessstructured) environments;

 objective, low-cost performance characterization;

 interoperability and modularity;

 intuitive interfaces; and

 modeling and simulation.

Technology infrastructure includes infratechnologies and

technology platforms (see Table 1-1). It is often the case that 

the public sector supports the majority of technology 

infrastructure research because of its public-good content 

(Tassey, 2008).

 

9 Technologies with varying degrees of public good content.
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Table 1-1. Definitions of Key Concepts

Term Definition Examples

Technology 

infrastructure

The broad base of quasi-public 

technologies and technical 

knowledge that support the R&D 

and production efforts of firms, 

universities, and laboratories, as 

well as the development and 

adoption of improved products, 

processes, and services.

• Infratechnologies

• Technology platforms

Infratechnologies A varied set of “technical tools” 

that include measurement and test 

methods, artifacts such as 

standard reference materials that 

allow these methods to be used 

efficiently, scientific and 

engineering databases, process 

models, and the technical basis for 

physical and functional interfaces 

between components of systems 

technologies such as factory 

automation and communications.

• Standard reference materials

• Process models

• Techniques for process and quality 

control

• Calibration services

• Traceability of measurements and 

test methods

• Benchmarks and testbeds for 

characterizing a new technology’s 

expected performance under realistic 

conditions

• Objective characterization of 

performance attributes of component 

technologies

• Reference datasets of common 

human poses

Technology 

platforms

Precompetitive proofs of concept 

that demonstrate the potential 

commercial viability of a new or 

improved product, process, or 

service. A characteristic of a 

technology platform is that it will 

often be foundational to multiple 

products and processes, generally 

from multiple firms.

• Bell Labs’ transistor proof-of-concept 

using solid state physics principles

(Tassey, 2008)

• Prototype networks such as ARPANET 

and NSFNET that led to the Internet

(Tassey, 2008)

• Open-source software such as Robot 

Operating System (ROS), ROSIndustrial, and OpenCV

• Standard perception engines for 

sense data types and sense data 

fusion (euRobotics, 2014)

Proprietary 

technologies

Commercialized products, 

processes, and services that may 

be derivatives of technology 

platforms and have been 

influenced by infratechnologies. 

Some proprietary technologies may 

have de facto quasi–public good 

characteristics, but they are not in 

the scope of this analysis.

• Industrial robots

• Collaborative robots

• Machine vision systems
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1.1.1 Infratechnologies

Historically, NIST has focused resources on this aspect of 

technology infrastructure. Infratechnologies are a varied set of 

“technical tools” that include measurement and test methods, 

artifacts such as standard reference materials that allow these 

methods to be used efficiently, scientific and engineering 

databases, process models, and the technical basis for physical 

and functional interfaces between components of systems 

technologies such as robotics and automation technologies. As 

written in Tassey (2008), “[c]ollectively they constitute a 

diverse technical infrastructure, various types of which are 

applied at each stage of economic activity.” New 

infratechnologies often replace less efficient forms of

infratechnology that support current standards (Tassey, 2008).

Infratechnologies influence the development of technology 

platforms and proprietary technologies. They also support 

efficient R&D, production, and market transactions such as 

complying with customer requirements and regulations.

Infratechnologies provide the technical basis for standards that 

are set using consensus standard-setting processes that are 

usually led by industry organizations. Their benefits include full 

disclosure of information, reduced uncertainty regarding 

product attributes, and an overall improved level of trust that 

helps to reduce market transaction costs.

The provision of infratechnologies requires a combination of 

industry and government investment because infratechnologies 

have substantial public good content (Antonelli & Link, 2015). 

Some industries depend on hundreds of distinct 

infratechnologies and associated standards. Furthermore, a 

particular infratechnology may have spillover benefits for many 

industries.

1.1.2 Technology Platforms

Technology platforms are precompetitive proofs of concept that 

demonstrate the potential commercial viability of a new or 

improved product, process, or service. These fundamental 

technical concepts originate from basic science research and 

can even be enabled by measurement infratechnologies (Link & 

Scott, 2010).

A characteristic of a technology platform is that it will often be 

foundational to multiple products and processes, the scope of 
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which is typically broader than the business model of any one 

firm. Therefore, no firm is able to fully appropriate the benefits 

of investing in the development of a technology platform, so 

achieving the socially optimal level of investment will generally 

require additional public investment.

1.2 PROPRIETARY TECHNOLOGIES

Proprietary technologies are commercialized products, 

processes, and services that may be derivatives of technology 

platforms and have been influenced by infratechnologies. 

Generally, firm investments in proprietary technology fall under 

the category of R&D spending. Proprietary technologies that are 

relatively ubiquitous may have quasi–public good 

characteristics though they are almost exclusively funded and 

developed by private-sector firms. These technologies are in-

scope to the extent that the technology infrastructure on which 

we focus enables their development and adoption.

1.3 SCOPE OF THE ANALYSIS

This report identifies gaps in technology infrastructure inhibiting 

the development and adoption of advanced robotics and 

automation in the U.S. manufacturing sector, and it quantifies 

the prospective economic benefits associated with addressing 

those gaps. The report also outlines specific potential 

opportunities for NIST to accelerate the development and 

adoption of critical technology infrastructure.

The research supporting this report was informed by primary 

data collection that consisted of interviews with experts in the 

robotics and automation value chain.10 It also was informed 

through a secondary collection of industry information.

To ensure that a variety of perspectives are accounted for, RTI 

spoke with a cross-section of experts in various stakeholder 

groups. We interviewed 84 individuals from industry 

associations and research centers (hereafter, observers); 

manufacturers of robotic systems, robotic component 

technologies including hardware and software, and related 

 

10 The value chain concept is a broader concept than the supply chain. 

Value chains include any stakeholders that add value to the end 

product or process, whether through providing goods, services, 

knowledge, coordination, and so on. 
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automation technologies (hereafter, developers); system 

integrators; and end users of robotics within the manufacturing 

sector. We specifically focused on end users in automotive 

manufacturing, aerospace manufacturing, electronics 

manufacturing, metal product manufacturing, process-oriented 

manufacturing, and other discrete parts manufacturing.

1.4 BARRIERS TO DEVELOPMENT AND 

ADOPTION

A motivating factor for this study is that private investments in 

innovation and diffusion of new technologies typically generate 

social value in excess of their private returns. As a result, some 

socially productive technology investments are not undertaken 

because private firms do not perceive the research as

profitable.11

The rate and extent of development of robotics technologies 

with the needed capabilities, as outlined in Section 1.4, and the 

rate and extent of their adoption in advanced manufacturing 

applications, will depend on the parallel development and 

diffusion of technology infrastructure that is generally 

underprovided by the market. This resulting market failure—the 

failure of the market to allocate a socially optimal level of 

infrastructure—provides an opportunity to improve the 

efficiency of economic outcomes through public investments in 

technology infrastructure.

Table 1-2 lists eight barriers to investment identified in the 

literature.12 These barriers that bring about market failure are 

present for robotics and can be expected to result in a 

reduction of overall economic welfare unless they are addressed 

through public support or other means. Each barrier describes 

general R&D market failures, and some barriers are specific to 

technology infrastructure.

Advanced robotics, like any emerging technology, has yet to 

exploit fully all relevant scientific knowledge. The probability of

 

11 The private rate of return is less than what is required (the private 

hurdle rate), even though the social rate of return exceeds that 

required by society (the social hurdle rate). 12 The taxonomy of barriers presented here draws insight from Link 

and Scott (2010) and Jaffe (2005).
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Table 1-2. Barriers to Developing and Adopting New Technology That Bring about Market 

Failure

Barrier

General R&D 

Market 

Failures

Market 

Failures with 

Regard to 

Technology 

Infrastructure

Inability to appropriate all social benefits, such as positive 

network externalities ● ●

Scope of commercial applications is broader than the market 

strategy of any one firm ● ●

Risk that R&D outcomes will be technically insufficient 

(technical risk) ●

Risk that R&D outcomes, although technically sufficient, will not 

be received well by the market, thereby providing an 

unacceptable return on investment (commercial or market risk)

●

Long and uncertain lag between R&D investments and returns ●

Asymmetric information between developers and adopters of 

new technology ● ●

Difficulties in bringing together component technologies from 

different industry segments ● ●

Industry structure, such as network externalities, presenting 

market-entry barriers to new technology ●

developing any commercially successful product is low, but

increasing the probability of technical success through using 

robotics technology will certainly increase the probability of 

commercial success. As we argue in this report, firms, and 

SMEs in particular, face a barrier to adopting robotics 

technology because of the cost of robotics technology and the 

associated learning curve. Public sector support of relevant 

technology platforms will help to lessen if not outright remove 

this barrier.13

Although it is difficult to rival the clarity of the 1950s Bell Labs 

demonstration of the concept of the transistor—a classic 

example of a technology platform—perhaps a parallel example 

for advanced robotics would be a standard perception engine 

that assimilates various sensor inputs and then the platform 

reasons what to do (how, for instance, to incorporate sensory 

 

13 Tassey (2010) provides an excellent discussion of the roles of 

infratechnologies and technology platforms in innovation.
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input into motion planning for moving an arm or grasping an 

object). Adequate development of technology platforms could 

help industry begin to bridge the gap from the science of 

knowledge representation and reasoning (KR&R) to 

commercially viable advanced robotics technologies. For 

example, euRobotics AISBL (2014) suggests that KR&R, which 

is a subfield of artificial intelligence, may have unexplored 

potential for robot autonomy.

Open-source efforts also play an important role as technology 

platforms for advanced robotics. NIST can contribute to and 

help accelerate such efforts. OpenCV and Robot Operating 

System (ROS)/ROS-Industrial (ROS-I) represent two important 

open-source efforts in robotics:

 OpenCV is an open-source computer vision and machine 

learning software library with more than 47,000 people 

in the user community and more than 7 million 

downloads. Well-established companies and startups 

alike are using OpenCV computing infrastructure and 

algorithms for sophisticated machine vision 

applications.14

 ROS is a modular collection of tools, libraries, and 

conventions that simplify robot programming in a way 

that can be applied across different robot systems. ROSI extends the capabilities of ROS to the manufacturing 

environment. ROS and ROS-I are both open-source 

projects.15

Private investment in R&D to bring forth a new or advanced 

technology depends on the expectation that the technology will 

find a market. This requires that manufacturers—the end users 

of advanced robotic systems—can hire skilled workers who are

ready to program, maintain, and repair those systems and to 

work alongside them. Adequate expected return on these R&D 

investments also depends on standards for safety, 

performance, and interoperability to allow emerging 

technologies to be exploited to their fullest potential.

The existing regulatory framework (e.g., Occupational Safety 

and Health Administration regulations) should reference and 

incorporate new standards—standards for safety, performance, 

and interoperability—to experience the full benefits of advanced 

 

14 http://opencv.org/ 15 http://rosindustrial.org/
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robotics technology. This framework is starting to emerge with 

recent Robotic Industries Association (RIA) safety standards but 

is far from complete. Safety standards, for instance, must take 

into account the capabilities of new technologies to allow safe 

applications that fully exploit the technologies’ potential. These 

standards can then be used to implement safety controls clearly 

and consistently to manage risks for robot integrators and end 

users. Performance standards and interoperability are needed 

to reduce the cost of developing and using advanced robotic 

systems and to reduce transaction costs (and technical risk) 

arising from information asymmetries among developers and 

end users. Reducing the cost of integrating new technologies 

into manufacturing processes will increase the expected return

and thus the level of future investment in development and 

commercialization, as well as the pace of technology adoption

by manufacturers.

Cultural acceptance will require confidence that individuals will 

not be harmed by using the technology in accordance with 

applicable safety standards. Beyond being safe, the interaction 

with robots must be natural and intuitive if the technology is to 

gain widespread acceptance. How best to achieve this is very 

much an open question among developers of robots. One 

developer put the question this way:“What is the right 

metaphor for robots interacting with people?”16

1.5 NEEDED CAPABILITIES AND TECHNOLOGY INFRASTRUCTURE

As described previously, advanced robotic systems are in many 

ways becoming more flexible and versatile than traditional 

industrial robots. However, a lack of critical technology 

infrastructure inhibits the development and adoption of 

advanced robotics.

RTI has identified six broadly defined industry needs, or 

capabilities, as being necessary to fully and efficiently apply

advanced robotics and automation technology in manufacturing 

industries (Table1-3). Additionally, examples of underlying 

 

16 Drawing an analogy to the question of what robots working side by

side with people ought to look like, this observer pointed out,“Your 

car does not pretend to be a horse.”
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technology infrastructure to help meet the needed capabilities 

are outlined.

The six advanced capabilities in Table 1-3 are discussed at 

length in Sections 5–10 with reference to barriers to innovation

that bring about market failure, potential impacts of meeting 

technology infrastructure needs, and potential roles for NIST.

In these discussions, references are made to specific comments 

by interviewees.

Table 1-3. Required Capabilities for the Application of Advanced Robotics in Manufacturing 

and Associated Infratechnology Needs

Industry Capabilities

Examples of Infratechnology to Help Meet 

Needs

Safe human-robot interaction (HRI)

Universal standards for developers of robotics

technologies and the application of these 

technologies in manufacturing settings with 

robots working in close proximity to people (see 

more below on sensing/perception for 

unstructured environments, relevant for 

intuitive HRI)

• Test protocols, objective scientific and 

engineering data, reference databases, and 

other technical inputs into standards for safe 

HRI (power/force-limiting, speed/separation 

monitoring, hand-guided operation, safetyrated monitored stop)

Sensing and perception for unstructured 

(or less-structured) environments

Improved perception (and the ability to plan 

and re-plan the robot’s actions based on what it 

“sees” and “knows”) gives a robot greater 

autonomy, lessening its demand that its work 

environment meet stringent tolerances

• Sensor registration and calibration

• Performance characterization (benchmarks, 

testbeds, and technical inputs to standards to 

characterize the performance of systems, 

subsystems, and components)

• Sensing/perception engines/architectures

• Proof-of-concept robotics applications of 

knowledge representation and reasoning

Intuitive interfaces

Enabling rapid programming and training 

without specialized skills

• Protocols to simplify the programming, 

training, and rapid re-tasking of robots

• Standard programming language for industrial 

robotics analogous to SQL or HTML

Interoperability and modularity

Plug-and-play for system components, enabled 

by standards for physical and electronic 

interfaces and software interfaces or translators

• Objective technical inputs into the standardsetting process: scientific and engineering 

data, benchmarks, testbeds, objective thirdparty testing of candidate technologies and 

configurations

Modeling and simulation

Virtual factory floor allowing modeling and 

simulation, calibrated based on real-time data 

feed from robots, machine tools, sensors, and 

control systems on the floor

• Robust, open, real-time operating system on 

the factory floor

• Reference models, modeling frameworks to 

fully integrate robots into models of the 

manufacturing environment and enable robust 

simulation/prediction

Objective, low-cost performance 

characterization

Making it easier for robotics users to know what 

they are buying and for developers and 

suppliers to show what their systems do

• Common performance metrics, objective data, 

testbeds, test methods, and benchmarks to 

characterize the performance attributes of 

advanced systems, subsystems, and 

components

Note: The term infratechnology was used in the interview process and is fully defined in Section 1.1.
Section 1 

— Overview of Robotics and Automation 1-13

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1.6 WHAT DISTINGUISHES THIS REPORT

A multitude of studies investigate the potential impacts of 

advanced robotics. For example, a recent study by the Boston 

Consulting Group (Sirkin et al., 2015) stated that global labor 

costs could be reduced by 16% by 2025 because of advanced 

robotics and that labor productivity in terms of output per 

worker would rise by 30%. Another estimate pegs productivity 

gains at 25% when manufacturers adopt automation, robotics, 

and vision systems (Crawford, 2014). Other benefits include

greater flexibility and improved quality.

Both of these studies and the insight from those we interviewed 

demonstrate that robotics and automation technology can yield 

significant cost savings, but there is less understanding of what 

is inhibiting the development and adoption of robotics 

technology.If barriers to adoption do exist, the question is 

What role can government play in mitigating these barriers and 

accelerating the penetration of robotics technology in the U.S. 

manufacturing sector? By addressing this question, this study 

represents a departure from most existing studies of robotics

because it specifically focuses on needed technology 

infrastructure, which, because of its public good characteristics, 

is in the purview of NIST.

This report provides relevant quantitative and qualitative 

information for NIST to consider when prioritizing investments 

and research activities to support U.S. manufacturing. We also 

approximate national economic impacts associated with 

enhanced technology infrastructure. Although the confidence 

bands around such estimates are wide because of the many 

inherent uncertainties associated with prospective interview-

based studies, these estimates nevertheless provide a general 

sense of the magnitude of potential benefits.

The remainder of this report is organized into the following 

sections:  Section 2: Analysis Methods and Primary Data 

Collection

 Section 3: Industry Trends and Technology Gaps  Section 4: Quantitative Results and Economic Impact 

Analysis
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 Sections 5–10: Discussions of the six needed 

capabilities, including potential impacts of meeting 

needs, sources of market failure, and roles for NIST

 Section 11: Conclusion
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2 Analysis Methods 

and Data Collection

This section presents our analytical approach to collecting and 

analyzing industry data and interview responses. These data 

were analyzed quantitatively using economic models that 

estimate the economic impact that enhanced technology 

infrastructure would have on the U.S. manufacturing sector.

2.1 DATA COLLECTION

Our data collection process began with selecting relevant 

sectors, identifying contacts within those sectors with the 

appropriate level of expertise, conducting detailed interviews 

with those contacts, and identifying a detailed North American 

Industry Classification System (NAICS) code associated with 

each interviewee’s industry.

2.1.1 Selection of Relevant Sectors

We selected relevant sectors for end users based on the 

manufacturing sectors where robotics technology is used or is 

expected to be used more broadly as the result of improved 

capabilities and enhanced infratechnology. The selection of 

sectors is relevant for end users only.

2.1.2 Interviews

Interviews were preferable to other alternatives such as online 

surveys because of the complex, nuanced topics being studied. 

We believe that the quality and richness of information needed 

in this study were obtainable only through interviews during 

which we could provide prompts and explanations to the 

interviewee as needed.

Interviews were primarily conducted over the phone and lasted 

for approximately 1 hour each. For phone interviews, we 

provided the interview guide several days before the interview 
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to help interviewees become better acquainted with the scope 

of our study. These interview guides are in Appendixes A and 

B.17 Some interviews were conducted in person at industry 

events and conferences.

We identified potential interview respondents by first identifying 

specific developers and end users. We then searched for key 

personnel within those firms. To supplement this contact list, 

we also identified individuals through professional associations, 

industry association membership, and conference and meeting 

attendee lists, among other sources.

Respondents represented a broad set of industries that develop 

and use robotics and automation technologies. Respondents 

varied in seniority from skilled engineers to middle 

management to executives. Some examples of the job titles for 

interviewees were:

 Senior engineering manager

 Director, advanced manufacturing technology

 Senior automation engineer

 Process automation engineer/process modeling and 

optimization engineer

 CEO/president

 Founder

 Chief technology officer/chief technical advisor

 Global lead for manufacturing

2.1.3 Developers versus End Users

Interview questions were different for developers and for end 

users. Technology developers were asked to provide 

quantitative responses regarding the impact of 

 

17 Two separate guides were used to differentiate between positions in 

the value chain—whether a firm develops and sells robotics 

technology or whether a firm uses robotics technology. Some firms 

are hybrid developer-users, and in these cases, we focused on the 

end user perspective but asked about both. We also spoke with 

systems integrators. Because integrators assist end users in setting 

up new robotic workcells, we interviewed integrators from the end 

user perspective. Interview questions asked about expectations for 

economic impact of applicable robotics and automation 

manufacturing technologies in terms of the percentage changes in 

their firm’s capital and labor, energy, and materials costs, as well 

as ancillary measures such as the cost of integrating robotics 

technology.
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— Analysis Methods and Data Collection 2-3

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infratechnologies on industry sales. They were also asked to

provide qualitative information on the specific infratechnologies

that NIST can potentially provide. Sales impact estimates are 

used below to quantify the influence of public investments in 

infratechnologies on the size of the overall market and the 

extent to which end users will adopt the technologies.

End users provided quantitative responses about how robots 

and other automation would change the costs of four factors of 

production—capital, labor, energy, and materials (KLEM). They 

also were asked to provide qualitative feedback on barriers to 

adoption, key technical pain points, and how robotics and 

automation technologies could improve their products. Specific 

NAICS codes were identified for each end user interviewed so 

that we could separate responses by industry.18

The following sections outline how the quantitative responses 

were used.

2.2 ECONOMIC MODELS

Using the quantitative estimates provided by developers and 

end users associated with having the capabilities enabled by 

infratechnologies listed in Table1-2 met, we estimated the 

impact for the United States using industry data and 

assumptions about applicability.

Specifically, respondents were asked to provide a percentage 

impact estimate for each quantitative response, with ranges 

being acceptable. In cases where a respondent provided a 

range, we used the midpoint of the range. In cases where a 

 

18 The industries represented by the respondents were identified by 

querying a variety of sources, including the following: • Hoover’s: If the firm was listed in the database, the firm’s 

activities were well contained within a NAICS code, and the 

classification matched our knowledge of the firm. 

• Census NAICS Web site search with information provided by — the respondents about the division within their firm that they 

represent, or

— descriptions of activities from company Web sites and/or 

annual reports.

Some respondents provided a NAICS code for their line of business. 

These responses were verified with a secondary search of the sources 

above. NAICS codes were typically identified at the narrowest level 

possible (i.e., five- and six-digit). Responses were then aggregated at 

the three- and four-digit level using applicability factors provided by 

respondents.
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respondent provided a single point estimate, that estimate was 

used as if it were the midpoint.

2.2.1 Developers

Developers came from a variety of backgrounds, including

industrial robot suppliers, collaborative robot suppliers, 

manufacturers of robot component technologies and vision 

systems, and robotic software developers.

National Impacts

For developers, we considered several methods to approximate 

the national impact of the provision of the infratechnologies 

noted in Table 1-3 (and in the interview guide) on the robotics 

market.

Developers provided estimates of the increase in their firm’s 

sales that would result from the provision of identified 

infratechnologies. In some instances, developers offered an 

opinion not only about the impact on their firm, but also on the 

industry as a whole. We use this information to infer the extent 

to which the developers’ responses apply to the industry. When 

we use the term “industry applicability” below, this is what it 

refers to.

Weighting each response by industry applicability, we applied

the average responses to the RIA’s 2014 estimate of U.S. 

shipments of $1.5 billion dollars (see Figure 3-1 for the 

trajectory of the market over the last 10 years). We adjusted 

the $1.5 billion figure upward by a factor of 3 to include the 

additional costs of ancillary products and services such as 

integration ($4.5 billion).19

2.2.2 End Users

Respondents were asked a question with reference to 

Table 1-3. Specifically, each respondent was asked to consider 

a hypothetical scenario: to consider the implications for KLEM if 

NIST was immediately able to meet each of the defined 

capabilities in the table through the provision of identified 

infratechnologies.

 

19 The other methods that we considered were based on data from end 

users. We considered using the net change in capital costs as a 

lower bound for the potential increase in the sales of robotics or the 

total estimated benefits/value to end users as an upper bound for 

the potential increase in sales of robotics.
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It is important to note that not all respondents were willing or 

able to provide quantitative estimates of impacts on costs. For 

example, one end user stated that enhanced robotics and 

automation technology would improve the yield in 

semiconductor and electronics manufacturing; however, this

end user was unable to provide a percentage impact for 

materials costs. These kinds of responses could not be included 

in the final quantitative assessment. For respondents who 

provided an estimate for at least one KLEM category, we 

conservatively assigned zero impact to the categories for which 

they did not respond.

National Impacts

The percentage impact estimates were assumed to apply to 

each respondent’s four-digit NAICS code. Table 2-1 summarizes 

the four-digit NAICS codes in our interview sample.

Respondents provided impacts for higher and lower levels of 

industrial classification, but the four-digit level was the 

minimum level at which estimates were summarized. We 

assume that responses at the five- and six-digit levels apply to 

the four-digit level. Respondents providing impacts for higher 

levels of aggregation (e.g., three-digit level) had their 

responses applied to all four-digit NAICS codes within the 

three-digit NAICS level that were not already represented by 

other respondents.

• Food manufacturing

• Wood product manufacturing

• Plastic products manufacturing

• Alumina and aluminum 

production and processing

• Machine shops; turned product; 

and screw, nut, and bolt 

manufacturing

• Other fabricated metal product 

manufacturing

• Computer and peripheral 

equipment manufacturing

• Semiconductor and other 

electronic component 

manufacturing

• Household appliance 

manufacturing

• Motor vehicle manufacturing

• Motor vehicle body and trailer 

manufacturing

• Motor vehicle part 

manufacturing

• Aerospace product and part 

manufacturing

Table 2-1. End User 

Industry Coverage
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The number of responses varied by four-digit manufacturing 

NAICS code. Overall, we received quantitative responses for 

four-digit industries that represent 36.5% of the sales of the 

manufacturing sector.20 We believe that other manufacturing 

industries for which we lack responses would benefit from the 

provision of the identified infratechnologies. This underrepresentation makes our estimates conservative.

Respondents were also asked to provide industry applicability 

factors for their impact estimates. For the vast majority of 

respondents, these factors were 100% (i.e., respondents 

expected their impact estimates to hold for all firms in the 

industry). Responses less than 100% were often stated as a 

fraction of the industry or for a particular type of firm or set of 

firms within the industry. When a specific fraction of the 

industry was not provided, we relied on secondary estimates 

such as market shares from sources such as IBISWorld reports.

First, we looked for outlying impact estimates that were judged 

to be unrealistic or heavily biased upward.21 After looking for 

outliers, we considered several possible ways to summarize the 

responses within each NAICS code, including the following:

 Average: a simple average of the midpoint impact 

response for all respondents

 Sales-weighted: a weighted average of midpoint impact 

responses based on the size of the respondent’s firm

We decided to use the simple average method because it was 

less susceptible to outlier influence.

To estimate cost-to-sales ratios for each KLEM factor input, we 

used industry data from national accounts provided by the 

Bureau of Labor Statistics (BLS) for energy and materials cost

estimates.22 The data provided by BLS gave highly aggregated 

accounts for capital and labor. To better identify capital and 

 

20 Sales for the manufacturing sector are defined as all sales within 

NAICS 31–33. 21 We did not exclude any responses for end users. 22 Energy costs included the manufacturing industry’s purchases of oil 

and gas extraction (NAICS 211), coal (NAICS 2121), electricity 

(NAICS 2211), natural gas (NAICS 2212), and refined petroleum 

(NAICS 324). Materials costs included purchases from other 

manufacturing industries in the NAICS range 3210–3330, excluding 

324 (refined petroleum and coal).
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labor costs associated with shop floor activities, we relied on 

the 2013 Annual Survey of Manufactures.23

Using industry data on cost-to-sales ratios, we estimated the 

KLEM national factor expenditures for each industry in 

Table2-1. We then reduced the national factor expenditures for 

each industry by the average industry applicability factor, which 

yielded a national factor expenditure for each industry. Then we 

applied the average percentage impact on costs to the national 

factor expenditure for each industry. This adjustment yielded

industry-level cost impacts for each industry for each of the 

four factor inputs. The total cost impact across industries is the 

sum of the industry-level impacts.

We apportioned the total cost impact to each of the six 

capabilities in Table1-3 by using the average share of 

importance points awarded to each capability. We used only the 

importance scores provided by end users (see Section 3.3 and 

Appendix C on importance scores).

For example, if an interviewee awarded a total of 20 points 

across the six capabilities, and he or she provided the highest 

importance score of 5 to safe HRI and 2 for objective, low-cost 

performance characterization, then the shares would be 25% 

and 10%, respectively. We then averaged these shares across 

individuals and applied them to the total cost impact.

2.3 CONSERVATIVE NATURE OF THE ECONOMIC MODELING APPROACH

The quantitative economic impact estimates calculated in this 

study are considered to be conservative in that they do not 

capture all the benefits that would result from an improved 

technology infrastructure. As will be discussed below, our 

analysis focuses on reductions in manufacturers’ production 

cost that would result from meeting the identified technology 

infrastructure needs. However, this does not capture all of the 

potential economic benefits associated with an enhanced 

technology infrastructure.

 

23 Capital costs include capital expenditures on machinery and 

equipment, computer and peripheral equipment, and other 

machinery and equipment. Labor costs include production workers’ 

annual wages grossed up to include nonwage benefits such as 

health insurance, retirement, and other fringe benefits.
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For example, a streamlined infrastructure for creating, 

transmitting, analyzing, and communicating design and 

production data would accelerate the development and 

commercialization of altogether new product markets. These 

new products would have increased economic value stemming 

from enhanced attributes, such as greater functionality, lower 

maintenance costs, and increased life expectancy.

However, valuing new (yet to be defined) products or product 

attributes is difficult, has great uncertainty, and is beyond the 

scope of this study.

An improved technology infrastructure will also lead to reduced 

R&D costs. However, interviewees were not able to quantify 

R&D savings, saying that the benefits would be a mixture of 

improved/accelerated R&D and enhanced product quality. 

Hence, these categories of benefits are discussed qualitatively 

but are not included in the quantitative economic impact 

estimates.

In general, focusing on manufacturing cost savings implies that 

the analysis captures primarily gains in producer surplus and 

does not capture gains in consumer surplus associated with 

improved product quality. In addition, the analysis does not 

capture increases in social welfare from increased output 

(sales), which result from lower cost and higher demand. The 

analysis also does not capture increased exports that would 

result from the enhanced competitive position of U.S.

manufacturers.

For these reasons, the economic impacts presented are 

considered to be conservative, lower-bound estimates. These 

estimates should also be interpreted as benefits per year. 

Benefits were quantified for a single year using recent industry 

data at NIST’s request; enhanced technology infrastructure 

would last significantly longer than just 1 year.
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3 Industry Trends and 

Technology Gaps

More robots are used in factories today than at any other time 

in history, and global and U.S. outlooks show that purchases of 

robots are expected to increase in the near term.

The use of robots has largely been limited to factories in the 

United States and other industrialized nations such as Japan 

and Germany, but shipment data show that China is now one of 

the largest buyers of robots in the world and some developing 

nations like Mexico have increased purchases of robots. The 

vast majority of the global stock of robots falls into the class of 

traditional industrial robots.24 Traditional industrial robots have 

also been highly concentrated in automotive manufacturing 

rather than having a broad-based pattern of adoption across 

manufacturing industries.

A new generation of cutting-edge collaborative robots is 

starting to reshape the market for manufacturing robots by 

expanding the applications that robots can be tasked with and 

by removing some of the barriers that have prevented broader 

adoption. Collaborative robots—which are a relatively novel 

product offering in the market—provide a new option along the 

price-performance gradient that may allow more SMEs to begin 

to experiment with robotics with less risk. This trend is 

reinforced by the long-term trend of an improving performance-

to-price ratio for industrial robotics that has been in motion 

since the early 1990s.

Although current trends signal increased adoption of robotics in 

the near term, sources of market failure still impede the

 

24 We have previously defined traditional industrial robots to mean the 

large, powerful, fast-moving robots that operate in safe, guarded 

space, cordoned off from factory workers.
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efficient development and adoption of robotics technology. To 

understand the barriers that bring about this market failure,

and the initiatives that NIST might pursue to alleviate them, 

RTI gathered perspectives from stakeholders positioned along

the robotics and automation value chain.

3.1 TRENDS IN ROBOTICS AND AUTOMATION

By the end of 2014, about 1.5 million industrial robots were in 

operation around the world (International Federation of 

Robotics [IFR], 2015). Japan has the largest installed base of 

robots of any country in the world, with the United States 

ranking second. The RIA estimates that the stock of industrial 

robots in use in U.S. factories is 230,000 as of 2014, which 

represents a 60% increase since 2004 (see Figure 3-1).

Figure 3-1. U.S. Stock of Robots and the Nominal Price per Robot, 2004–2014

Source: Robotic Industries Association (2004–2014).
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The cost of robots has been falling, thus making robots more 

accessible to SMEs. Furthermore, a new generation of 

“collaborative” robots is being offered at a lower point along the 

price-performance gradient. The availability of this new 

generation of robots helps to lower the barrier to entry for 

manufacturers that have not yet adopted robotics technology.

Sales of robots in the United States appear to be cyclical and 

depend on the investment cycles of U.S. industries, in particular 

the automotive industry. However, many experts expect non-

automotive industries to begin increasing their demand for 

robots. Figure 3-2 shows that the industry contracted during 

the Great Recession (which lasted from December 2007 to June 

2009 [National Bureau of Economic Research, Public 

Information Office, n.d.]) and reached a 10-year low during 

2009. The robotics industry has since recovered, and in 2014, 

25,425 robots were sold to companies in North America, 

yielding $1.5 billion in revenues for robotics suppliers in North 

America. The volume of robots sold and the revenue generated 

are record highs for the industry. The volume of new orders 

suggests that the industry will continue to expand at least in 

the near term.

Global Outlook

IFR (2015) expects that there will be a 15% compound annual 

growth rate (CAGR) in global robot installations from 2016 to

2018. Robot purchases are expected to be strongest in Asia/

Australia, followed by the Americas and Europe (see Table3-1).

The main customer of industrial robotics globally is the 

automotive sector. Given its purchasing power, the automotive 

sector is arguably the single largest customer for robotics 

technology. The electrical/electronics industry is also 

substantially increasing its global investments in robotics (IFR, 

2015). Other industries that have recently been increasing 

orders globally include rubber and plastics manufacturing, pharmaceutical manufacturing, food and beverage 

manufacturing, and metal and machinery manufacturing.
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Figure 3-2. Shipments of Robots to North American Customers, 2004–2014

Source: Robotic Industries Association (2004–2014).

Geography Forecast CAGR, 2016–2018

Asia/Australia 18%

Americas 10%

Europe 10%

Global 15%

Source: International Federation of Robotics (2015).

A recent analysis by the Boston Consulting Group considered 

two factors that will influence how quickly different industries 

adopt robots in the future. The first factor is the costeffectiveness of substituting robots for human labor. The 

second factor is the degree to which production tasks are 

automatable—which was defined as occupational tasks that 

could potentially be replaced by available technology. This 

emphasis yielded four industries that are most likely to lead 

adoption globally: computers and electronic products; electrical 

Table 3-1. Forecast 

CAGR in Industrial 

Robot Installations, 

2016–2018
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equipment, appliances, and components; transportation 

equipment; and machinery(Sirkin et al., 2015).25

U.S. Outlook

The U.S. outlook largely mirrors the global outlook. The 

automotive industry has been the primary driver of growth in 

the use of robots in the United States and in most industrialized 

nations. Non-automotive industries that increased orders in 

2014 by the largest percentages were plastics and rubber, 

semiconductors and electronics, and metals. The fastest 

growing applications for robot orders in North America (United 

States–specific data not available) in 2014 were welding (arc 

and spot), assembly, and material handling.

Venture capital investments in robotics technology, a potential 

indicator of future growth of the U.S. robotics market, nearly 

tripled from 2011 to 2013 according to a report by 

PricewaterhouseCoopers (2014).26

3.1.1 Robot Performance-to-Price Ratio

Notwithstanding the differences between traditional industrial 

robots and the new generation of advanced collaborative 

robots, prices of robots have fallen substantially since 1990. For 

example, without adjusting for quality, prices for robots used in 

electronics manufacturing decreased by roughly 45% between 

1990 and 2005. With quality adjustments, prices decreased by 

nearly 80% during this time frame (Mathia, 2010). Based on 

these trends, Kent Massey of HDT Global equates this to a 

doubling of the performance-to-price ratio every 4 to 10 years

depending on the particular assumptions that are made

(personal communication, June 13, 2014).

Integration costs are also starting to fall. Based on our 

conversations with industry experts, the total cost of robotic 

systems (initial costs plus integration costs) tends to be 3 to 4

 

25 It is important to note that enhanced technology infrastructure, 

which is the focus of this study, may influence industries’ propensity 

to adopt robots in ways different from those outlined in the Boston 

Consulting Group report (Sirkin et al., 2015). Nevertheless, this is a 

useful framework for thinking about the likelihood of adoption 

moving forward. 26 The influx of venture capital investment in the robotics market will 

likely spur innovation for robotics for the manufacturing 

environment and for robotics in the consumer products, agriculture, 

logistics, public safety, health care, and service industries.
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times the initial costs of each robot; however, that ratio is 

changing. Integration costs include ancillary products and 

services required to get the robot up and running. IFR (2013) 

estimates a 3X multiplier for ancillary products and services, 

which is in line with the opinions of industry experts.

This high fixed cost has been a barrier to adoption for SMEs. An 

interviewee expressed his opinion about adoption costs of a 

robot in the following way: “Traditional robotics has always 

required an integrator, but now with collaborative robots you 

don’t need a system integrator and that’s what really reduces 

the cost of the robot.”

The trend toward lower integration costs benefits all 

manufacturers, especially SMEs. Furthermore, with the growth 

of collaborative robotics, a broader range of offerings is starting 

to emerge along the price-performance gradient. Low-cost, 

lower-payload robots that somewhat break the mold of

traditional industrial robots are typically offered at lower price 

points. Additionally, some robotics companies have made 

efforts to simplify the integration process as part of this 

collaborative trend.

Notwithstanding, technology infrastructure needs remain, and if 

provided, would further lower the total cost of adoption.

3.1.2 Traditional Industrial Robotics versus Collaborative 

Robotics

Advanced robotics is distinguished from traditional industrial 

robots by capabilities such as adaptable and reconfigurable 

assembly, autonomous navigation, dexterity (a balance of 

precision and compliance in robotic hands, or end effectors), 

perception suitable for semi-structured and unstructured 

environments, and the capability to work safely in close 

proximity to people.27 As articulated by the Advanced 

 

27 These capabilities are highlighted in A Roadmap for U.S. Robotics—

From Internet to Robotics (Robotics-VO, 2013), which also 

discusses model-based integration and design of a supply chain, 

nano-manufacturing, and green manufacturing. Some elements of 

these capabilities are also discussed here. For example, supply

chain modeling and integration connect this section of the report 

with its companion report (Economic Analysis of Technology 

Infrastructure Needs for Advanced Manufacturing: Smart 

Manufacturing Processes). As another example, improving the 

energy efficiency of robotic systems (consistent with green 
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Manufacturing Partnership, which falls under the Advanced 

Manufacturing Initiative and the proposal for a National 

Network for Manufacturing Innovation, advancements in 

enhanced robotics capabilities could potentially increase the 

competitive position of the U.S. manufacturing sector in global 

markets, specifically through reducing manufacturing costs and 

enhancing the quality and customization of manufactured 

goods.

Traditional industrial robotics represents a relatively mature set 

of technologies. Developers of industrial robots have learned 

over time the best ways to build these systems. System 

integrators and end users in manufacturing have similarly 

learned over time how to deploy them. Advanced robotics, by 

contrast, is an emerging technology area characterized by a 

high degree of technical and market risk, including the best 

ways to build new functionality into robotic systems and the 

best ways to deploy robots with greater functionality, 

particularly where HRI or collaborative robotics is involved.

Traditional industrial robotics are well suited where there are 

large production runs (for which the high cost of commissioning 

the robotic systems for a single run can be amortized over 

hundreds of thousands or even millions of units) and where 

automated processes can be isolated spatially, or caged off, 

from areas where people need to be working. The potential for 

advanced robotics to add value to the manufacturing sector is 

great, especially for small production runs with frequent line 

changes (agile manufacturing) and for situations where it would

be economically advantageous for robots and people to work 

side by side (collaborative robotics).28

 

manufacturing principles) and improving the safety of robots 

working in close proximity to people are linked because light

weighting of robotic arms is critical to both capabilities. 28 The availability of more flexible and versatile robotic systems may 

be especially advantageous for SMEs. In the United States, 

manufacturing establishments with fewer than 250 employees 

represent 57% of manufacturing employment and 46% of wages 

(Bureau of Labor Statistics, Quarterly Census of Employment and 

Wages, based on 2013 first-quarter manufacturing employment). 

The higher average wages for production workers in larger 

establishments are consistent with higher labor productivity 

resulting from the typically higher degree of automation. This 

suggests at least a prima facie case that wages should be expected 

to rise at smaller establishments as advanced robotics technology 

makes its way into the market.
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The distribution of the current generation of robots (primarily, 

traditional industrial robots) by manufacturing applications 

(Figure 3-3) and by industry (Figure 3-4) provides insight into 

the current use of traditional industrial robots and potential

opportunities for the next generation of advanced robots. For 

example, traditional industrial robots are used extensively in 

the automotive industry, particularly in body and paint shops. 

Final assembly, however, which accounts for roughly one-half 

of an automaker’s production schedule, is still performed 

almost entirely by hand. Automating final assembly tasks would 

involve a high degree of HRI. Collaborative robotics technology 

and associated technology infrastructure has not yet advanced 

to point where these tasks could be automated.

A similar practical difficulty inhibits the automation of aircraft 

assembly. Workers are frequently present in and around the 

area in which the aircraft is being built, thus making the use of 

traditional industrial robots somewhat unsafe. The same is true 

for electronics manufacturing.

Figure 3-3. Distribution of Robots in the United States, by Major Application Area (Based on 

2012 U.S. Shipments)

Source: International Federation of Robotics (2013, p. 70).
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Figure 3-4. Distribution of Robots, by Industry (Based on 2012 U.S. Shipments)

Source: International Federation of Robotics (2013, p. 70).

The emerging trend toward using advanced robots for agile 

manufacturing and collaborative interaction is reflected in the 

greater number of less expensive, lower-payload robots sold in 

the United States over the last decade. From the perspective of 

one industry observer, a decade ago, a much larger fraction of 

traditional industrial robots sold in the United States had 

payloads in the range of 50 to 200 kilograms and cost between 

$150,000 and $250,000; whereas today, there are more lowercost robots with payloads of 5 to 50 kilograms.29 Furthermore, 

when accounting for the cost of integrating a traditional 

industrial robot into a workcell, the total cost of the system can 

typically be many times the initial cost of the robot, making the 

technology cost-prohibitive for SMEs that do not have sufficient

production volumes across which fixed costs can be amortized. 

Many industry observers and participants believe that a

potential advantage of collaborative robots is that they would 

be easier to integrate with existing production lines because 

their size and payload mimic those of humans and the software 

 

29 The average price of industrial robots sold in the United States has 

fallen from around $80,000 in 2000 and 2001 to between $60,000 

and $65,000 from 2009 through 2012 (adjusted for inflation) (IFR, 

2013, p. 55).
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interfaces tend to be more user-friendly. More specifically, an 

interviewee noted, “One anticipated consequence of this new 

generation of collaborative robots is that they can be deployed 

in unstructured environments. [Manufacturers] don’t have to 

bolt the equipment to the floor, cages, etc. That whole model 

will still exist, but the collaborative model will allow the user to 

take on the integrator role, and allow the robot to be deployed 

in different locations based on need.”

Some collaborative robots are claimed to be inherently safe 

because they are physically lighter and manage a smaller 

payload, but this is not necessarily the only way to achieve safe 

human-robot collaboration. One end user described 

participating in a demonstration with a robot capable of 

supporting a 250-kilogram payload while being manipulated by 

hand. One software developer who works closely with end users 

of manufacturing robots described participating in 

demonstrations in which an “intelligently safe” robot adjusted 

its speed or motion paths (in extreme cases, stopping 

altogether) to accommodate people moving in and out of its 

workspace. At a collaborative robotics conference, one 

company demonstrated a collision avoidance technology where 

a robot arm not only slowed down and stopped when it sensed 

a human arm in its range of motion, but it also moved out of 

the way when the human arm tried to initiate contact.

Another key aspect of collaborative robots is the ease of 

programming and setup. Some collaborative robots such as 

Rethink Robotics’ Baxter and Universal Robots’ UR330 have 

more friendly interfaces for setup, although this is not an 

industry standard.

Another area of growing interest in collaborative robotics is 

combining robot arms and end effectors with mobile robot 

platforms, but many technical hurdles remain to be solved 

before these systems are widely available. Several industry 

experts expressed the view that the technology for safe HRI in 

applications like these and others is developing rapidly but that 

 

30 Rethink Robotics, based in Boston, Massachusetts, sells the Baxter 

and Sawyer robots. Universal Robots, based in Denmark, sells the 

UR10, UR5, and UR3 robots.
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safety standards would need to catch up with the technology 

before it can be fully used.31

3.2 INDUSTRY CAPABILITIES AND POTENTIAL 

BENEFITS

Table 3-2 reproduces the information in Table 1-3 for 

convenience, with an additional column for the potential 

benefits associated with each capability being realized.

Table 3-2. Required Capabilities for the Application of Advanced Robotics in Manufacturing, 

Associated Infratechnology Needs, and Benefits

Industry Capabilities

Examples of Infratechnology to 

Help Meet Needs

Potential Benefits and 

Impacts

Safe human-robot interaction 

(HRI)

Universal standards for developers of 

robotics technologies and the 

application of these technologies in 

manufacturing settings with robots 

working in close proximity to people 

(see more below on 

sensing/perception for unstructured 

environments, relevant for intuitive 

HRI)

• Test protocols, objective 

scientific and engineering data, 

reference databases, and other 

technical inputs into standards 

for safe HRI (power/forcelimiting, speed/separation 

monitoring, hand-guided 

operation, safety-rated 

monitored stop)

• More flexible, smallerfootprint production lines

• New and creative use cases of 

robots working in close 

proximity and in collaboration 

with people

• Lower integration costs

• Improved safety

• Reduced market risk for 

developers

• Reduced liability for end users

• Increased adoption of 

collaborative robots

Sensing and perception for 

unstructured (or less-structured) 

environments

Improved perception (and the ability 

to plan and re-plan the robot’s 

actions based on what it “sees” and 

“knows”) gives a robot greater 

autonomy, lessening its demand that 

its work environment meet stringent 

tolerances

• Sensor registration and 

calibration

• Performance characterization 

(benchmarks, testbeds, and 

technical inputs to standards to 

characterize the performance 

of systems, subsystems, and 

components)

• Sensing/perception 

engines/architectures

• Proof-of-concept robotics 

applications of knowledge 

representation and reasoning

• Lower integration costs 

associated with

accommodating tolerances

• Flexible navigation of 

unstructured or lessstructured environments

• More flexible plant layouts

• Improved safety

• Optimized robot motions

• Data streams to calibrate 

simulation models

(continued)

 

31 Some simple implementations of human-safe robots are already in 

use. In automotive assembly, for example, automated guided 

vehicles (AGVs) deliver parts from one area of the factory to 

another. Sensors cause the AGVs to stop when a person steps into 

their path or when they encounter unexpected contact. See, for 

example, this video: 

http://www.youtube.com/watch?v=cCR0Bmw5TxI.
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Table 3-2. Required Capabilities for the Application of Advanced Robotics in Manufacturing, 

Associated Infratechnology Needs, and Benefits (continued)

Industry Capabilities

Examples of Infratechnology to 

Help Meet Needs

Potential Benefits and 

Impacts

Objective, low-cost performance 

characterization

Making it easier for robotics users to 

know what they are buying and for 

developers and suppliers to show 

what their systems do

• Common performance metrics, 

objective data, testbeds, test 

methods, and benchmarks to 

characterize the performance 

attributes of advanced 

systems, subsystems, and 

components

• Reduced uncertainty

• Improved understanding of 

new technologies

• Increased adoption of robotics 

by SMEs

Interoperability and modularity

Plug-and-play for system 

components, enabled by standards 

for physical and electronic interfaces 

and software interfaces or 

translators

• Objective technical inputs into 

the standard-setting process: 

scientific and engineering data, 

benchmarks, testbeds, 

objective third-party testing of 

candidate technologies and 

configurations

• Plug-and-play functionality

• Reduced integration costs 

(physical and software 

interfaces)

• Modular development of 

systems

• Increased adaptability of 

robotic systems

• Scalable, reconfigurable, and 

reusable robotic systems

• Reduced retooling costs

• Increased adoption in 

industries with small

production runs

Intuitive interfaces

Enabling rapid programming and 

training without specialized skills

• Protocols to simplify the 

programming, training, and 

rapid re-tasking of robots

• Standard programming 

language for industrial robotics 

analogous to SQL or HTML

• Simplified programming

• Reduced setup time and setup

costs

• Enables individuals without 

specialized training to 

commission a robotic system

Modeling and simulation

Virtual factory floor allowing 

modeling and simulation, calibrated 

based on real-time data feed from 

robots, machine tools, sensors, and 

control systems on the floor

• Robust, open, real-time 

operating system on the 

factory floor

• Reference models, modeling 

frameworks to fully integrate 

robots into models of the 

manufacturing environment 

and enable robust 

simulation/prediction

• Control of processes from 

central dashboard

• Improved prediction

• Adjustments can be optimized

• Reduced delay and work 

stoppage

• Software reconfigurable 

factory floor

• Reduced retooling costs

• Improved “as-built” 

documentation

• Using robot teaching to refine 

simulation models

The following subsections describe each of the six capabilities 

individually, the associated infratechnology that would help 

meet the needs of industry, and potential benefits of these 

capabilities being realized.

3.2.1 Safe Human-Robot Interaction

With traditional industrial robots, the risk assessment process 

and the onus of responsibility are well understood. The same 

cannot be said of safe HRI in the paradigm of advanced robots, 

especially collaborative ones. Infratechnologies such as 
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standards and methods to characterize safety are needed to 

address three fundamentally different approaches to, or models 

of, safe HRI: First is the power-limiting or force-limiting robot, 

typically smaller and lighter with a lower payload, which is 

unable to exert enough force to cause serious injury to a 

person; second is speed and separation monitoring to enable a 

robot of any size to perceive (using onboard sensors, area 

monitors or sensors, or some combination) a person entering 

its work area and accommodate that person by slowing down, 

entering a “soft-servo” state, or stopping completely; and third 

is hand-guided operation, or applications in which a robot 

augments the force applied by the operator—the operator 

provides the perception, dexterity, and judgment. The potential 

impacts of these approaches to safe HRI include the removal of

cages, which, in the long term, means more flexible, smaller-

footprint production lines, new and creative use cases of robots 

working in close proximity and in collaboration with people, 

lower integration costs, and improved safety.

Comments from industry are consistent about the need for 

multiple approaches to safe HRI. One developer suggested that “lighter is inherently safer,” using the example of a 4-pound

arm that is able to lift 16 pounds being safer than a 16-pound

arm that can lift 4 pounds. Others pointed out that safe HRI can 

be supported by other capabilities such as enhanced sensing 

and perception, simulation, and a better-trained workforce. For 

power- and force-limiting robots, research is needed to 

characterize materials suitable to replace cast aluminum and 

cast iron currently used in robotic arms. For speed and 

separation monitoring, research in sensing and perception is 

needed.

Safe HRI can make the manufacturing environment more 

productive by complementing the existing workforce. Dull, 

repetitive, and often ergonomically taxing tasks can be 

performed by the collaborative robot while a worker monitors 

the collaborative robot and focuses on higher value-added 

tasks. One end user stated that with the right training and 

development of user-friendly interfaces, collaborative robots 

could potentially be labor enhancing rather than labor 

replacing. Although large potential benefits can be reaped from 

safe HRI, it may also be out of reach of SMEs because there are 

not currently any low-cost tools for measuring safety in this 

new paradigm.
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The first robot safety standard (not human-robot safety 

standard) was published in 1986 and was updated in 1992 and 

1999. The 1999 standard was active until 2013. Overall, the 

safety regime from 1986 to 2013 has been to eliminate HRI 

with no consideration of any potential foregone benefits. Since 

about 2006, there has been acknowledgment that HRI will be 

the next standard, and it has gained more attention over the 

last few years. As one industry observer noted, interactions in 

the field are still very conservative in terms of how far the 

working envelope is being pushed. The same observer noted 

that many assumptions being made could be deemed 

conservative in nature, or they could be based on the average 

industrial worker who does not really exist. The ability to tailor 

assessments of safe HRI to the unique characteristics of a 

particular application is the goal that industry would like to 

work toward, but many challenges, including a lack of 

infratechnologies, may hinder progress in this direction.

The International Organization for Standardization (ISO), the 

American National Standards Institute (ANSI), and the RIA are 

the principal organizations involved in promulgating safety 

standards for robotics. The standard-setting process involves 

collaboration among developers and users of robotic systems 

with the involvement of NIST. Standards for safe HRI have 

existed since 2006, but the current standard (ANSI/RIA R15.06, 

the U.S. adoption of ISO 10218) devotes fewer than 10 pages 

out of a 150-page document to collaborative robot applications. 

ISO has been working on Technical Specification 15066 (TS 

15066), informed by research conducted at the University of 

Mainz, which provides more guidance for the use of power- and 

force-limiting robots. This standard was originally planned for

release in 2015, but it required additional development and was 

only recently released in February 2016.32 According to a recent 

blog post summary of TS 15066 by a gripper manufacturer, the

new technical specification adds three items to the existing 

standard:

 Data on maximum allowable robot speed associated with 

specific human pain levels for various body parts

 

32 http://www.iso.org/iso/home/store/catalogue_tc/ 

catalogue_detail.htm?csnumber=62996
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 More in-depth explanations of the different modes of 

human-robot collaboration

 What to include in collaborative robot risk assessments

(Bélanger-Barrette, 2016)

Standards for other models of safe HRI, such as hand-guided 

operation and speed and separation monitoring, are considered 

to be more futuristic. Having the language describing these 

models opens the door for future development, but widespread 

commercial applications are not expected in the immediate 

future.

The Fraunhofer Institute for Manufacturing Engineering and 

Automation in Germany has also worked on producing data on 

the collision of robots and humans in an industrial environment; 

this is described by the term dynamic impacts. In a technical 

paper, authors from the Fraunhofer Institute compare the need 

for standardized safety tests for robots to the crash testing that 

is standard in the automotive industry: “Whereas it is widely 

accepted in the automotive industry to take a standardized 

sample of crash tests scenarios that are evaluated on 

anthropomorphic test devices, so called crash test dummies, 

robotics science still lack uniform tests that can be carried out 

for different systems” (Oberer, Malosio, & Schraft, 2006).

Uniform safety tests for automobiles were not required in the 

United States until the 1970s, more than a century after 

automobiles were invented and more than 4 decades after 

Henry Ford’s Model T was widely available. U.S. experience in 

automobile safety shows that a sufficient body of science had to 

accumulate before safety standards were established. A similar 

body of science might have to accumulate for robotics, but 

NIST could accelerate the development of fundamental 

metrology needed for standard robotic safety tests.

Next-generation collaborative robots will require next-

generation safety standards, which will need to be informed by 

objectively evaluating the capabilities of collaborative robots, 

with standards consistently applied across companies. In short, 

there is a need for “critical technical inputs to standards,” as 

discussed in the Roadmap for U.S. Robotics (2013, p. 22). The 

world’s first traditional industrial robots were installed in U.S. 

automotive plants in the early 1960s, but collaborative robots 

have only recently entered the market. The newness of the 

technology suggests that much more basic research needs to 
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be done to understand HRI. As the body of science grows and 

standards for safe HRI are formulated and adopted by the 

manufacturing sector, there will also be a need for low-cost risk 

assessment tools and simulation models that will help end 

users comply with safety standards and better understand and 

quantify the potential risks of HRI.

Companies that are now developing collaborative robots selfassert the safety of the robots based on their own metrics

stating, for example, that a robotic arm is not capable of 

exerting a force greater than a certain threshold and is 

therefore inherently safe. The absence of universal force 

measurement standards makes it difficult for end users to 

compare product offerings and choose the best solution to meet 

their needs.

Moving toward universal standards for safety would also give 

developers a common target, which would reduce the market 

risk faced by developers (the risk that a technically successful 

solution would fail to find a market large enough to provide an 

adequate return on investment). The infrastructure needed to 

test and certify that robotic systems meet universal standards 

would also be an asset to developers aiming to meet the 

standards. Datasets or libraries, for example, of common 

human poses and motions would be helpful in developing 

technology to make robots better able to perceive the behavior 

of people around them and in testing that a new system meets 

a universal standard for this capability.

Understanding how common aspects of an application 

contribute to safety is critical. To reduce liability, manufacturers 

often rely on a third-party integrator to come in, set up the 

workcell, and do a thorough risk assessment. One developer 

pointed out that one of the main benefits of collaborative robots 

is reducing the need for integrators. Integration serves to shift 

risk away from the end user to the company doing the 

integration to some degree, but it comes at a high cost. 

Manufacturing companies may hire an integrator (either the 

robot manufacturer’s integration team or a third-party 

integrator) because of liability concerns associated with setting 

up collaborative workstations on their own. One industry 

observer pointed out the need to apply safety standards and 

certifications to entire integrated systems, not only to individual 

robots; the point being that it is possible to integrate two 
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inherently safe robots in a way that may not be safe at the 

systems level.

One end user indicated that the limited reach and payload of 

human-safe robots currently on the market are limiting factors 

to adoption. A real opportunity would be to have a robot that 

could support a heavy part in a “soft-servo” state that allows a 

person to manipulate the part and perform detailed work, then 

return control to the robot for further processing.

3.2.2 Sensing and Perception for Unstructured Environments

The current limitations of the ability of robots to sense and 

perceive individuals, obstacles, and other objects constrain the 

extent to which robots can be moved from structured workcells 

into unstructured or semi-structured factory environments. 

Improved perception of its environment may enable a robot to 

be more flexible. One end user offered the example of a wire 

form that the robot must bend into an exact shape. Tolerances 

of 1/16th of an inch were acceptable for the wire form, but the 

robot would enter a fault state and thus stop working if it did 

not find the form within 3/1,000ths of an inch of where it 

expected it to be. In general, accommodating such high 

tolerances involves costly and time-consuming effort to design

a robot’s environment. Greater flexibility afforded by improved 

sensing and perception capabilities could lower these costs.

Improved sensing and perception depend on the technical 

capabilities of hardware combined with software and related 

algorithms. Sensing and perception includes vision systems and

tactile perception systems. Sophisticated examples of vision 

systems include object differentiation through object 

recognition and object characterization (surface body 

characteristics, inertial characteristics), and the ability to track 

humans in the manufacturing environment. Two- and three-

dimensional sensing technologies are relevant depending on the 

particular use case. Sophisticated examples of tactile 

perception systems include methods of force sensing and robot 

dexterity, which are more futuristic and have potentially large 

benefits.

There is a need to better characterize the performance of 

different types of sensors in different factory environments. 

Infrared sensors, for example, may not perform optimally in the 

presence of fiberglass, which absorbs infrared light. In general, 
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it would be useful to characterize the visibility of different 

materials to different kinds of sensors. Materials that are 

optimized for robot visibility could be incorporated into work 

smocks, for example. euRobotics AISBL (2014) asserts that the 

most important barrier to perception ability “is the limitation of 

the sensor technology for accurate measurement of specific 

materials (reflective, absorbing, and transparent) using off the 

shelf, affordable and eye-safe sensors. Fusing these different 

modalities together into a common representation is also not 

generally solved. Currently, common sense knowledge is 

integrated only at higher level systems, but methods are 

missing to select which information to use at the sensor fusion 

level” (p. 103).

Sensing that relies on wireless communication (e.g., area 

sensors that track the movement of people on the factory floor) 

may be subject to interference. One end user described having 

problems with arc-welding scrambling wirelessly transmitted 

data and suggested that it would be useful to have objective 

data characterizing the robustness of wireless communication 

systems to the types of interference frequently encountered in 

factory environments. The need for robot-specific wireless 

communication protocols, suitable to handle latency 

requirements, remote haptic feedback, data security, and 

cloud-processing of high-level cognitive functions, is discussed 

in the Robotics 2020 Multi-Annual Roadmap (euRobotics AISBL,

2014, p. 148).

The need for cognitive architectures for the “unification of 

perception, planning, and control for physical human robot 

interaction” (euRobotics AISBL, 2014, p. 133) applies more 

broadly to functioning in unstructured environments. The 

challenge could have nothing to do with human interaction but 

rather (to take an example shared with us by a manufacturing 

systems research manager at a large U.S. automaker) with 

“flexible end-effector servo-driven locating and clamping.” The 

capability to assimilate various sensor inputs and to reason 

what to do (how, for instance, to incorporate sensory input into 

motion planning for moving an arm or grasping an object) may 

depend on “standard perception engines for sense data types 

and sense data fusion” (euRobotics AISBL, 2014, p. 164).

euRobotics AISBL (2014) suggests that KR&R has many 

potential applications for robotics. Examples include “the 
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representation of higher level concepts in semantic maps, and 

the use of ontologies to enable robots to elicit information from 

the Web” (p. 171).

Sensing and perception for unstructured or semi-structured 

environments is critical to safe HRI. Furthermore, sensing and 

perception data can be used to optimize robot movements and 

feedback data into simulation models.

3.2.3 Objective, Low-Cost Performance Characterization

The lack of objective data characterizing the performance 

attributes of advanced robotic systems is an inhibiting factor to 

their adoption, particularly for SMEs that are less likely to be 

able to bring multiple robot technologies in house for internal 

evaluation and comparison. There is a need for objective 

evaluation using common benchmarks, testbeds, test protocols, 

and metrology with traceability to standards maintained at 

national laboratories.

End users would like to refer to objective data on the 

performance of systems subjected to standard test procedures 

or in standard testbeds that model a factory environment 

similar to theirs. For example, the performance of a robotic arm 

can be described in terms of its repeatability (of 0.1 millimeter, 

for example) and accuracy (of 0.5 millimeter, for example).33

These metrics are commonly understood and sufficient to 

describe the capabilities of a robot’s arm, but there are 

presently no such standard metrics for the performance of 

robotic hands. What are the metrics (analogous to repeatability 

and accuracy for robotic arms) that are relevant for describing 

the dexterity of a robotic hand?

Low-cost performance characterization also includes related 

automation systems such as control systems that commonly 

interact with robots. Other areas where performance may be 

difficult to characterize include sensing and perception, 

autonomous mobility, wireless data transmission, safe HRI, and 

energy efficiency. One observer pointed out that the energy 

efficiency of robotic systems is less easily characterized than 

that of, say, an electrical appliance. The efficiency of the robot 

 

33 Repeatability can be defined as the ability of a robot to achieve 

repetition of the same task. Accuracy is the error between the 

desired task and the actual task obtained by the robot (Joubair, 

2014).
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will depend on its programming and the conditions under which 

it is used. This issue raises the need for sophisticated testbeds 

and test methods that can provide users with relevant insight 

into a new technology’s expected performance under realistic 

factory conditions.

3.2.4 Interoperability and Modularity

The lack of interoperability across the robotic systems of 

different vendors (and among robots, machine tools, control 

systems, and sensors) is costly to end users and inhibits the 

adoption of advanced robotics technology.

As one developer described, “Getting the robotic arms and end 

effectors from any manufacturer to work and communicate 

easily would be a big step toward broader adoption.” One 

collaborative robot manufacturer described lengthy efforts to 

persuade a single supplier of end effectors to adhere to a 

specific gripper schematic. The supplier was hesitant perhaps

because other customers followed different schematics. An 

industry standard (de facto or established) would help solve 

this issue. This sentiment was noted more generally as applied 

to other peripheral devices, such as vision systems. Every

interviewee who made this point agreed that an analogy to USB 

devices was appropriate. Many added that robotics does not yet 

have true plug-and-play capability (which most now take for 

granted when purchasing a printer or camera to attach to a 

laptop), but having such capability would be valuable to end 

users.

However, companies that develop industrial robots may have 

incentives to limit interoperability due to customer lock-in. One 

developer candidly pointed out that the benefits of 

interoperability to end users would outweigh any cost of 

standardization to robotics manufacturers. One gripper 

manufacturer in particular noted that no single company can do 

this alone. Lock-in and market power are problems of 

proprietary standards. Public standards eliminate this barrier to 

innovation, and NIST, as well as other organizations such as 

the Institute of Electrical and Electronics Engineers (IEEE),

could have a potential role to play in accelerating public 

standards.

In addition to standards for physical and electronic interfaces, 

interoperability requires standard software interfaces, or 
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translators. Each robotic system manufacturer has its own 

programming language. This complicates the integration of 

many pieces of automated equipment into a production line, 

making it more costly and time consuming to set up each line. 

A research team leader at a heavy equipment manufacturer 

described the problem of having to write XML34 code to allow an 

Ethernet-enabled robotic platform to communicate with an 

MTConnect-enabled35 machine tool as 

“not difficult but a 

hassle.” Although this company was large enough to have at 

least one full-time employee with such expertise, many SMEs

would have to hire contractors to perform such work. The need 

for specialized expertise and training increases the cost of 

setting up a new line with robotics, making it impractical to 

automate some production activities that involve small batches 

and frequent line changes.

An example of a common language standard that allows 

different robots to communicate and share data is PackML,36

which is used primarily for automated packaging. It is a 

communication protocol, analogous to an Internet protocol, like 

IPv6. MTConnect is another example of such a communications 

protocol, ostensibly capable of facilitating plug-and-play 

interconnectivity between robots, machine tools, sensors, and 

other devices. An engineering manager at a heavy equipment 

manufacturer described MTConnect as being useful in 

conjunction with production monitoring systems (like 

SCADAware and Freedom eLOG) but subject to limitations 

(e.g., handling data updates in the seconds range, whereas 

programmable logic controllers in machine tools handle data 

updates in the submillisecond range). The need for 

interoperability also extends to the connection of robots with 

other software and enterprise systems such as manufacturing 

enterprise solutions systems, enterprise resource planning 

systems, and programmable logic controllers. Increased 

interoperability reduces costs for end users and allows for a 

more competitive, more innovative marketplace.

 

34 XML is an acronym for Extensible Markup Language. 35 MTConnect is an acronym for Machine Tool Connect. The MTConnect 

standard enables production machines, sensor packages, and other 

manufacturing equipment to provide data in standardized formats rather than proprietary formats. 36 PackML is an acronym for Packaging Machine Language.
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The broader vision of those in industry is toward open 

architectures and standard interfaces to enable the modular 

construction of systems and the development of a component 

marketplace. euRobotics AISBL (2014) points toward “modular 

system architectures with well-defined interfaces” and 

“architectures based on multifunctional sub system blocks, for 

example allowing integrated sensing and actuation or multi 

modal sensing” (p. 124). Modularity will enable scalable, 

reconfigurable, and reusable robotic systems (as opposed to 

process monuments). One benefit of modularity will be to 

reduce the cost of retooling (changing machinery, fixtures, and 

tools to produce a different model vehicle, for instance). In the 

automotive and heavy equipment industries, retooling 

represents a significant cost, which could be reduced by 

increasing the adaptability of robotic systems. In industries 

where smaller production runs and frequent line changes make 

the cost of retooling prohibitive to automation, modular robotic 

systems that lower retooling cost could make automation costeffective and lead to greater adoption.

euRobotics AISBL (2014) asserts, “Formalisms and algorithms 

over different modules (perception, planning, learning, 

envisioning etc.) are typically incompatible. The top-performing 

state-of-the-art modules are often the hardest to integrate, 

because they use sophisticated and incompatible 

representations and algorithms that must first be adapted to 

the needs of robot control” (p. 116).

ROS-I is an effort to enhance automation interoperability by 

allowing robots, manipulators, end effectors, sensors, mobile 

platforms, and other devices to communicate with one another 

using a single language. Consortia in the United States and 

Europe have formed to support the further development of 

ROS-I. The U.S. consortium includes large end users like 

Boeing, Ford, BMW, Caterpillar, and 3M, as well as prominent 

developers such as Yaskawa Motoman and Siemens. NIST is 

also a member of that consortium.

ROS-I represents an important infrastructure element that 

supports interoperability robotics and automation systems. 

However, individuals have pointed out several limitations to 

ROS (see Section 7.2).
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3.2.5 Intuitive Interfaces

One factor contributing to the time required to set up an 

automated line is programming the robots. We heard from 

many interviewees that the time and cost of setting up an 

automated line could be reduced significantly if robots could be 

programmed more intuitively, without the need to write many 

lines of code. Intuitive interfaces would allow individuals

without specialized training to effectively commission a robotic 

system.

One setup model involves a person manipulating the robot 

through the required motions so that the robot is then able to 

replicate the motions autonomously (e.g., Rethink Robotics’ 

Baxter). Another approach involves an intuitive graphical user 

interface (GUI) that allows robots and conveyors to be 

integrated with drag-and-drop instructions, with code auto-

generated in the background (e.g., ABB’s PickMaster).

One may think of GUI applications that allow an individual to

build a query of a relational database by selecting tables and 

connecting them with lines. The SQL37 code is generated 

without the user having to write a line of code by hand. 

Similarly, one can build a Web page using a GUI interface that 

writes HTML code in the background. There is presently no 

standard programming language for robots in general

analogous to SQL or HTML on which to build intuitive interfaces.

The ROS and ROS-I provide an open-source software platform 

with basic functionality. One developer described how his team 

had written what is called a ROS bridge to allow communication 

between ROS and the proprietary system of a large robotics 

manufacturer. Criticisms of ROS include that it is trying to solve 

too many issues at once, that it is currently more academic 

than industrial in nature, and that there are challenges with its 

ability to function with real-time fidelity.

3.2.6 Modeling and Simulation

Advanced robotics and automation systems play an important 

role in smart manufacturing systems. This linkage was most 

evident in conversations with interviewees from an automaker 

and a heavy equipment manufacturer who described similar 

visions for having the ability to virtually model and physically

 

37 SQL is the acronym for structured query language.
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control processes on the factory floor from a central dashboard. 

The virtual modeling environment would accurately emulate the 

physical manufacturing floor before any robots, machine tools, 

conveyors, sensors, and other devices and structures were in 

place. Then, after putting the equipment in place, the virtual 

models would receive real-time data from the physical 

manufacturing process to refine the simulations and improve 

the accuracy of their predictions.

In this scenario, potential adjustments (such as moving a weld 

point, changing a process to accommodate a change in a 

supplier’s part, or reallocating work when a robot has a 

mechanical problem) can be simulated, and an optimal solution 

can be implemented on the plant floor in real time, with 

minimal delay or work stoppage.

With reference to the automotive industry, to change from the 

production of one model of a vehicle to another for instance, 

the time and cost of retooling could be reduced by orders of 

magnitude if modeling and simulation capabilities allowed the 

factory floor to be software reconfigurable.

For this scenario to be realized, robots, machine tools, sensors, 

and control systems must be able to communicate. A research 

manager at a large automaker emphasized the need for an 

“open, real-time operating system on the factory floor.” For an 

in-depth exploration of these issues, see the companion report 

titled Economic Analysis of Technology Infrastructure Needs for 

Advanced Manufacturing: Smart Manufacturing Processes.

One expert from the aerospace industry indicated there may be

several benefits to measuring what is occurring in the physical 

world and then replicating that information in the digital world. 

For aircraft manufacturing, the as-built documentation is 

important, and a digital representation of the physical aircraft

adds value. Being able to simulate tool paths is important to 

this documentation. Another benefit of enhanced simulation is 

being able to flow back some of the robot teaching that is done 

through programming or teach pendants to the central 

simulation program. This is one way to avoid having to solve 

the same problem multiple times, and it enables a more 

accurate simulation.
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3.3 THE IMPORTANCE OF INFRATECHNOLOGIES 

TO SUPPORT ROBOTICS AND AUTOMATION

To provide a barometer for NIST in terms of how it might be 

able to accelerate the development and adoption of robotics

technology most effectively, interviewees were asked to 

characterize the importance of each of the six capabilities in 

Table 3-2 and corresponding infratechnologies. Safe HRI, 

sensing and perception for unstructured environments, intuitive 

interfaces, and interoperability and modularity appear to be the 

most important needs, whereas the interviewees evaluated 

modeling and simulation and objective, low-cost performance 

characterization as noticeably less important.

Following this question, interviewees were asked what they 

viewed to be the appropriate role for the public sector,

including potential roles for NIST, if any, to deliver these 

capabilities. Figure 3-5 summarizes the responses about the 

level of importance (measured as 1–5, where 1 represents the 

least important and 5 represents the most important) by 

capability. The percentage of interviewees who responded with 

a 4 or 5 are in darker shades, and the percentage of 

interviewees who responded with a 3 or below are in lighter 

shades. The average importance score is overlaid in the gray 

boxes.

For example, for the capability of safe HRI (the leftmost bar), 

the lighter portion of the bar represents the 27% of 

interviewees who responded with a 3 or below, and the darker

portion of the bar represents the 73% of interviewees who 

responded with a 4 or 5. The gray box represents the overall 

mean importance score of 4.2.
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Figure 3-5. Importance of Capabilities/Needs and Corresponding Infratechnology Needs

Note: The level of importance of each capability is measured on a scale from 1 to 5, where 1 represents the least 

important and 5 represents the most important.

If we interpret “important” as a 4 or a 5 and “not important” as 

a 1, 2, or 3, then the diverging stacked bar chart (Figure 3-6) is

an alternative way to visualize the importance scores that 

draws more attention to the relative sentiment across the six 

technical areas. The bold horizontal axis represents the baseline 

between scores of 4 or 5 and scores of 1, 2, or 3. The vertical 

axis represents the percentage of responses, where each 

increment represents 20%.

For example, for the capability of safe HRI (the leftmost bar), 

the lighter portions of the bar that lie below the bold horizontal 

axis represent the interviewees who responded with a 3 or 

below, and the darker portion of the bar that lies above the 

bold horizontal axis represents the interviewees who responded 

with a 4 or a 5.
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Figure 3-6. Importance of Capabilities/Needs and Corresponding Infratechnology Needs, 

Diverging Stacked Bar Chart

Note: The level of importance of each capability is measured on a scale from 1 to 5, where 1 represents the least 

important and 5 represents the most important.

As reflected in the distribution of importance scores in the 

figures, the majority of interviewees evaluated safe HRI, 

sensing and perception for unstructured environments, intuitive 

interfaces, and interoperability and modularity as being 

important; that is, they evaluated the importance of the 

capability with a 4 or a 5. The interviewees evaluated modeling 

and simulation and objective, low-cost performance 

characterization as noticeably less important.38

There is some degree of overlap between these capabilities. For 

example, technology infrastructure that supports objective, 

low-cost performance characterization can also make it more 

feasible to implement safe HRI. Several interviews noted 

interdependence among all of these capabilities. For example, 

modeling and simulation tools can support safe HRI. Another 

interviewee stated that better interoperability and modularity 

would unleash innovation in sensing and perception.

 

38 Appendix C has the importance scores broken out by developers and 

end users.
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Nevertheless, the level of importance of these capabilities 

might be viewed as a second-order indicator for the areas in 

which NIST can play a role in advancing the development and 

adoption of robotics technology. Perhaps a first-order indicator 

is the comments from interviewees on a potential role for NIST 

involvement to lead, coordinate, or encourage development in 

each of these areas. Sections 5–10 describe specific potential

actions for NIST to enhance technology infrastructure so that 

the benefits of robotics are fully realized.39 Before discussing 

roles for NIST, we quantify the economic impacts of realizing 

the needed capabilities through enhanced technology 

infrastructure.

3.4 STAKEHOLDERS IN ROBOTICS AND 

AUTOMATION

RTI developed a list of stakeholder groups to interview for our 

study in order to understand industry perspectives from 

different parts of the value chain, industry capabilities/needs,

and associated infratechnology needs identified in Table 3-2.

We also used these interviews to identify barriers inhibiting the 

development and adoption of robotics technology in 

manufacturing. In developing these stakeholder groups, we 

considered the structure of the robotics industry as it relates to 

manufacturing and the potential trajectory of the industry.

Table 3-3 shows the number of observers, developers, system 

integrators, and end users with whom we spoke for this study. 

The perspectives that each stakeholder group represents are 

discussed as follows.

 

39 Each of the six capabilities is discussed in the order of the highest 

stated importance to the lowest stated importance.
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Stakeholder Subcategory

Number of 

Interviewees

Observers Industry associations 5

Universities and research centers 7

Other 4

Developers Robotic systems 16

Industrial robots 7

Collaborative/mobile robots 8

Robotics hardware components 7

End effectors/grippers 6

Vision systems 2

Robotics software 8

Industrial automation 3

System 

Integrators

4

End Users 30

Aerospace 5

Automotive 10

Electronics 4

Metal products 6

Process 3

Other discrete parts 2

Total 84

Note: The number of interviewees reflects unique interviewee-interview 

instances.

3.4.1 Observers

Industry observers, such as those in industry associations and 

university research centers, provide valuable information about 

the trends in robotics and the potential impacts of improved 

infratechnology across manufacturing industries. For example, 

one industry observer had more than 3 decades of experience 

working with developers and end users in the robotics industry 

and had witnessed the industry evolve.

Observers often interact with developers and end users across 

multiple manufacturing industries. Observers are sometimes 

reluctant to provide quantitative estimates of economic 

impacts; however, they do provide rich qualitative insight. 

Although observers do not account for a large portion of our 84 

interviews, they provided important, broad-based insight about 

Table 3-3. Number of 

interviewees by Position 

in the Value Chain
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customer needs and technical challenges. Therefore, most 

observers were interviewed from the perspective of a 

developer.

3.4.2 Developers

From the developer stakeholder group, we learned how 

enhanced infratechnology will affect the R&D process and 

enable new capabilities and use cases for customers.

Developers would be directly using the technology

infrastructure and technology platforms that NIST could 

potentially provide or work to ensure that they are provided by 

other entities. Some of the developers also provided insight 

from the end user perspective because they work closely with 

end users in solving technical challenges.

Developers include companies manufacturing and selling entire 

robotic systems and robotics hardware components such as 

robot arms and end effectors. These companies—which 

comprise 41% of our interviews—are intimately familiar with 

the current state of the art of robotics technology. They 

perform in-house R&D to improve the capabilities of their 

robots and use technologies in the public domain to meet their 

new product development goals.

Other developers focus on robotics software tools. Interviewees 

suggested that some software development for industrial robots 

(especially algorithms for safe HRI) is performed in universities 

and nonprofit consortia, so we also reached out to researchers 

in these settings for additional information.

Finally, developers such as robotics R&D companies and 

research institutes focus on applied and long-range R&D.

3.4.3 System Integrators

System integrators have a unique perspective because they 

often work across multiple industry sectors and use multiple 

robotics vendors. They represent an important bridge between 

developers and end users, and they play an essential role in 

customizing solutions, although as one industry expert pointed 

out, integration of robotics as a practice has not been 

standardized: “Integrators consider every project as a new 

project. Smaller companies are creating the solutions. There is 

no standardized approach to integration, no intellectual 

bandwidth for R&D on the solutions side.” Integrators 

understand the technical pain points faced by end users that 
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infratechnologies could improve. At the same time, integrators 

have some influence on how new technologies are adopted and 

used. Integrators were interviewed from the perspective of end 

users.

There is some degree of vertical integration in the robotics 

value chain; some of the major robot suppliers also provide 

integration as a service, and some of the more sophisticated 

end users also do their own integration. As has been discussed 

in this report, it is possible that integrators will play less of a 

role moving forward as collaborative robots are adopted.

3.4.4 End Users

A large portion of our interviews were targeted at end users. 

This population includes companies that use robotics and 

automation in their operations and that could benefit from the 

additional capabilities that the next generation of advanced 

robotics technologies will provide. This population also includes

manufacturing companies that sparingly use the current 

generation of traditional industrial robots but could potentially 

use the next generation to a greater extent given its increased 

capabilities. In both cases, we sought to understand the 

capabilities and supporting infratechnology that would be 

needed to enable new applications.

Specifically, for current users, we sought to understand the 

limits of existing robotics technology and the applications of the 

new technologies enabled by the infratechnologies discussed in 

the interview process. For non-users, we sought to understand 

the barriers to development and adoption of new technologies

and how advanced robotics will affect the cost structures of 

existing industries.

RTI focused on end users in the manufacturing sector.40 The 

manufacturing sector comprises establishments that carry out 

mechanical, physical, or chemical transformation of materials, 

substances, or components into new products, or 

 

40 Although outside the scope of this study, several interviewees noted 

that service robotics and consumer robotics, while not as far along 

in terms of market adoption, are much larger markets than the 

manufacturing robotics market. One interviewee compared the 

current robotics market to the personal computing market before

PCs were adopted by households, which came much later than the 

business sector’s adoption of computers.
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establishments that assemble component parts into new 

products (U.S. Census Bureau, 2012).41 End users span all 

organization sizes, from small job shops to multinational 

Fortune 500 companies.

Our coverage for advanced manufacturing robotics and 

automation includes the end users in aerospace, automotive,

electronics, metal products, process, and other discrete parts

manufacturing. Table 3-3 shows the count of end users with 

whom we spoke, by industry group. These groups are based on 

industries having the same value chain (e.g., aerospace, 

automotive) or industries having similar production processes 

(e.g., electronics, metal products, process, other discrete parts

manufacturers).

Overall, speaking with stakeholders from various parts of the 

robotics and automation value chain provides us with a wellrounded view of market trends, barriers to development, 

barriers to adoption, and the infratechnologies that NIST can 

provide to support the needed capabilities outlined in Table 3-2.

 

41 In terms of the NAICS, manufacturing includes sectors 31, 32, and 

33. By this definition, the extraction of natural resources, 

agriculture, and construction are out of scope.
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Quantitative Results 

and Economic 

Impact Analysis

Based on interviewee responses, we estimate that enhanced 

infratechnology for robotics and automation would result in 

$40.4 billion in net cost savings for the U.S. manufacturing 

sector compared with current baseline national factor 

expenditures on four factors of production—capital, labor, 

energy, and materials(KLEM). These aggregate cost savings 

represent a 5.3% decline in shop floor KLEM expenditures 

across the aerospace, automotive, electronics, metal products, 

process, and other discrete parts manufacturing industries.

These savings are concentrated in labor and materials, which 

are partially offset by an increase in capital expenditure for 

robotics and automation technology.42 Next

-generation robotics 

and automation technology will make U.S. manufacturers more 

efficient and more flexible, making the U.S. manufacturing 

sector better positioned to compete internationally, which is a 

policy goal of the White House and federal agencies.

Developers will measure benefits in terms of the increased 

penetration of their technology in the marketplace. Robot 

suppliers and service providers believe that enhanced 

infratechnology will lead directly to increased demand from end 

users and thus increased sales. The RIA estimates the U.S. 

robotics market at $1.5 billion in sales in 2014 (RIA, 2014). 

Accounting for ancillary products and services—which are 

roughly two times the number of sales shipments—the overall 

market for robotic systems in the United States is roughly $4.5 

billion. Based on responses from interviewees, the U.S. robotics 

 

42 Energy costs are expected to increase as well because of the 

increase in the stock of robots, but these costs are negligible.
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market would grow by 48% if enhanced infratechnologies were 

in place. Accounting for ancillary products and services, the 

overall market could increase by as much as $2.1 billion. This 

figure is not included in the overall economic benefit because it 

represents a transfer of surplus from end user manufacturers to 

robot developers, the net social benefit of which is zero.

Nevertheless, the expected growth illustrates that enhanced 

infratechnology could substantially influence the adoption of 

robots.

4.1 FIRM-LEVEL DEVELOPER SALES IMPACTS

Developers of robotics and automation technology also stand to 

benefit from enhanced infratechnology in terms of increased 

market opportunities. Many developers with whom we spoke 

are able to make a direct link between enhanced 

infratechnology and increased sales of robotics technology 

because enhanced infratechnology would lead directly to 

increased adoption of robotics and automation.

For example, SMEs—which are more likely to use collaborative 

robots than traditional industrial robots—represent a largely 

untapped customer base. Also, large industries such as the 

aerospace sector have a lot to gain from enhanced 

infratechnology because robotics are typically used only at early 

stages of the production process (see vignette in Appendix D). 

Aerospace manufacturing does not have the repeatability of the 

automotive sector, which has limited the uptake of traditional 

industrial robotics in aerospace manufacturing, but one can

envision many use cases in the aerospace sector where more 

flexible robotics technologies could be deployed.

On average, developers cited a 55% increase in their sales that 

would be directly attributable to enhanced infratechnology in 

the six areas outlined in Section 2. Figure 4-1 shows the 

distribution of percentage changes in sales, which varies from 

0% to 300%.
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Figure 4-1. Percentage Change in Developer Sales Associated with Enhanced 

Infratechnology

Note: Each vertical bar represents one interview response.

4.2 NATIONAL-LEVEL DEVELOPER SALES 

IMPACTS

Scaling sales impacts up to the national level first requires 

adjusting the impacts by using the industry applicability factor 

that each interviewee provided. The average applicabilityweighted sales impact is 48%. This percentage should be 

interpreted to mean that the robotics market would grow by as 

much as 48%. NIST provided the underlying infratechnologies 

to enable the capabilities outlined in Table 3-2.

4.2.1 Market Size

As mentioned in Section 3.1, 25,425 robots were sold to 

companies in North America, yielding $1.5 billion in revenues 

for robotics suppliers in North America. This represents roughly 

14% of the global market in 2014.

To account for ancillary services and products such as 

integration, systems engineering, peripherals, and software, we 
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used a standard 3X multiplier to calculate the total size of the 

market, which is what IFR uses in its statistical reports. We 

believe the 3X multiplier is conservative based on information 

from our interviews with stakeholders.43

4.2.2 Total National Impacts for Developers

We estimate a $2.14 billion increase in U.S. sales of robotics 

technology because of enhanced infratechnologies.44 If the 

infratechnology needs were met today, the increase in sales is 

likely to occur over an approximately 5- to 10-year horizon as 

infratechnologies diffuse among market participants. Most of 

this increase is likely concentrated in the manufacturing sector, 

although some developers whom we interviewed sell to multiple 

sectors, and enhanced capabilities for manufacturing will have 

spillover benefits for other sectors.

Of the $2.14 billion increase, $713 million is presumed to be 

shipments of robot systems, whereas $1.4 billion is presumed 

to be ancillary products and services. Based on our sample of 

interviews, we expect significant purchasing to take place in the 

aerospace, automotive, and metal products sectors based on 

the expected benefits in these industries.

4.2.3 Other Impacts Not Quantified

Other developer impacts that have not been quantified in this 

study include the impact of technology infrastructure on R&D 

costs, R&D opportunities, accelerated improvement of existing 

products, and accelerated development of altogether new 

products.

Interviews suggested that, in fact, there would be real impacts 

in some of these areas if industry needs were met through 

enhanced technology infrastructure such as standards. 

However, limited quantitative information was provided in the 

interviews. Some developers said that their overall R&D 

budgets would not be reduced, but instead that it would free 

 

43 It is possible that the multiplier to arrive at the overall system cost

could decline over time. However, we did not specifically ask 

interviewees about the impact that improved technology 

infrastructure would have on this variable. We believe that the 3X 

multiplier is reasonable and conservative. 44 To calculate the national impact on sales for developers, the 

following calculation was done: $1.5 billion U.S. market for robots x 

3X multiplier = $4.5 billion market, including ancillary products and 

services x 48% = $2.14 billion increase in market revenues.
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them up to work on other things. Other developers suggested 

marginal decreases in costs but struggled to quantify them.

Other interviewees suggested that R&D opportunities (the 

likelihood of technical success) would increase because 

everyone in the industry would be “playing by the same rules.”

Furthermore, interoperability would help companies improve 

existing technologies and develop new technologies more 

rapidly because less time would be spent translating between 

systems and more time could be spent creating new interfaces.

4.3 FIRM-LEVEL END USER IMPACTS

During our interviews, respondents were asked to estimate the 

percentage change in KLEM factor inputs resulting from the 

potential adoption of improved robotics and automation 

manufacturing technologies, as well as impacts on products. 

Individual firm-level impacts are then aggregated and scaled to 

estimate potential national-level economic impacts.

Table 4-1 shows the average percentage change in factor 

inputs provided by respondents. Not surprisingly, robotics has 

the potential to reduce labor substantially: by 18%. The net 

percentage change in capital was positive, suggesting that end 

users think that enhanced robotics technology will result in 

increased purchases of robots. Note that the percentage change 

in capital costs reflects net capital expenditures, which include 

additional capital expenditures to implement robotics and 

capital savings due to increased efficiency. The average 

materials savings due to enhanced robotics and automation 

technology was 8%. Energy costs had a negligible increase on 

average, reflecting that more robots would need to be powered.

Factor Input Mean Impact of Having 

Industry Needs Met

K: Capital +22%

L: Labor −18%

E: Energy +1%

M: Materials −8%

Table 4-1. Percentage

Change in Factor Inputs

Due to Having Industry 

Needs Met
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Figure 4-2 shows the range of the raw responses from 

interviews broken out by each factor of production. For 

example, the top left panel represents the individual percentage 

responses about the impact on infratechnologies on capital 

costs. A few stories emerge from the panel of graphs. On 

average, capital costs tend to increase, but the figure indicates 

that in reality, the impact is much more nuanced and depends 

on the firm. One group of firms expects that capital costs will 

increase on net as more robots are purchased. Other firms 

expect that although more robots will be purchased, other 

forms of more expensive capital will be more efficiently used 

because of the benefits that robots provide, which drives down 

capital costs on net. Yet another group of firms expected no net 

change in capital costs.

In terms of the labor input, it is clear that robotics and 

automation technology reduce labor costs. This occurs through 

the reduction of workers needed or through the increased 

productivity of existing workers, although long-term 

manufacturing trends indicate that the U.S. manufacturing 

sector continues to produce more output with fewer workers. 

Ergonomically stressful or monotonous jobs are often the first 

ones that are targeted to be automated because of worker 

safety and quality concerns and associated costs.

For energy, there are three camps of responses. Most 

companies thought that energy costs would remain unchanged 

by enhanced robotics and automation. Another group thought 

that energy costs would be reduced between 8% and 15%. A 

third group thought that energy costs would increase between 

14% and 15% because of the increase in installed robots. 

Finally, for the vast majority of interviewees, materials costs 

were expected to decrease or remain unchanged. Reduced 

rejection rates due to the greater use of robotics is one 

example of why materials costs would decrease.

The percentage change in each of the factor inputs is then 

applied to estimated expenditures on the inputs for each firm to 

obtain a composite percentage impact on costs. This was 

conducted for each user interviewed estimating applicable firmlevel factor expenditures. Aggregating the firm-level factor 

impacts yields a single (weighted average) percentage change 

in production costs. Figure 4-3 shows the firm-level impacts for 

each respondent.
Section 4 — Quantitative Results and Economic Impact Analysis

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Figure 4-2. Range of Cost Impacts on Shop Floor Expenditures Associated with Enhanced 

Infratechnology, by Factor Input

Capital (K) Labor (L)

Energy (E) Materials (M)

Note: Each vertical bar represents the consensus from each interview.
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Figure 4-3. Range of Total Composite Cost Impact on Shop Floor Expenditures Associated 

with Enhanced Infratechnology

Note: Each vertical bar represents the consensus from each interview.

When accounting for the share of total expenditures on KLEM, 

the average composite cost impact is a 6.7% reduction. As 

shown in Figure 4-3, firm-level composite cost impacts vary 

widely. Although a few of the composite cost impact estimates 

show net cost increase, this could be the result of industry data 

not aligning well with individual firms’ cost structures or other 

cost dimensions that we did not capture in our interviews. We 

have kept these increases in our results so as to be more 

conservative.

The end user responses described in this section form the 

foundation for the national-level end user impacts in 

Section 4.4.

4.4 NATIONAL-LEVEL END USER IMPACTS

Overall, we estimate that enhanced infratechnology for robotics 

and automation would result in $40.4 billion in cost savings for 

the U.S. manufacturing sector. This cost savings is driven by 

the percentage changes from Section 4.3 and the total national 

factor expenditure represented by the industries in our sample.
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As Figure 4-4 shows, materials costs make up the majority of 

national factor expenditures for the industries addressed by our 

study. Materials costs are the primary cost driver, accounting

for 61% of total national factor expenditure for the industries in 

our sample. Labor costs and capital costs make up 25% and 

9%, respectively. Energy costs make up the remaining 5%.

The breakdown of costs has implications for our economic 

models. For example, a 1% cost savings for materials is going 

to have a greater absolute dollar value impact than 2% cost 

savings for labor.

The distribution of costs among factors of production varies 

across industry sectors in our sample. Figure 4-5 shows the 

share of the national factor expenditures accounted for by each 

factor input for aerospace, automotive, electronics, metal 

products, process industries, and the remaining industries in 

our sample. Electronics manufacturing is substantially more 

capital intensive than the other industries in our sample. In 

contrast, aerospace manufacturing is the most labor-intensive 

industry sector. Finally, automotive and the “other” remaining 

industries are the most materials-intensive industries.

Figure 4-4. KLEM Shares

of National Factor 

Expenditure
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Figure 4-5. KLEM Shares of National Factor Expenditure, by Industry Sector

Great care was taken to determine the appropriate quantity of 

KLEM expenditures to which we apply cost impacts, and we 

defined each factor input to hone in on shop floor expenditures. 

Furthermore, interviewees answered a question about the share 

of firms within the manufacturing sector for which the stated 

cost impacts are applicable. The average applicability factor 

was 90.1%.

Following the procedures outlined in Section 2, we scaled out 

sample impacts to the national level using a series of informed 

assumptions about how the responses in our sample mapped to 

U.S. industries. Table 4-2 illustrates our calculations with a 

stylized example for a particular NAICS code. These same 

calculations were carried out for the NAICS codes in our 

sample. The estimates for each NAICS code derived in Step 5 in 

Table 4-2 were then summed together to obtain estimates of 

national economic impacts.

Table 4-3 shows our results, broken down into the four factors 

of production. Overall, enhanced infratechnology for robotics 

and automation would result in an estimated $40.4 billion in 

cost savings for the U.S. manufacturing sector. This impact is 

driven by substantial labor and materials savings that are 

partially offset by an increase in capital investment and energy 

usage.
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Table 4-2. Stylized Example of Scaling Sample Impacts to the National Level, End User

Step Description Figures and Calculation(s)

Step 1—Begin with KLEM 

percentage impact 

responses.

Percentage changes in KLEM factor 

inputs are recorded during the 

interview.

K: +25%

L: -10%

E: +10%

M: -5%

Step 2—Calculate 

applicability-weighted cost 

impacts.

Multiply the industry applicability 

factor from the interview with the 

percentage changes in KLEM factor 

inputs to derive the applicabilityweighted cost impacts.

Applicability factor = 50%

K: +25% x 50% = +12.5%

L: -10% x 50% = -5%

E: +10% x 50% = +5%

M: -5% x 50% = -2.5%

Step 3—Assign an industry 

code and average responses 

from the sample.

Assign a 3- or 4-digit NAICS code 

based on the interviewee’s company 

and average the applicabilityweighted cost impacts from Step 2 

with other responses from the 

sample that are categorized with 

the same NAICS code, if applicable.

The NAICS code that best describes this 

interviewee’s company is NAICS 3329 Other 

fabricated metal product manufacturing.

No other responses in the sample have the 

same NAICS code, so the average responses 

are simply the figures from Step 2.

K: +12.5%

L: -5%

E: +5%

M: -2.5%

Step 4—Estimate KLEM 

expenditures for the 

industry.

Using Annual Survey of 

Manufactures and Bureau of Labor 

Statistics data on KLEM-to-sales 

ratios, estimate the KLEM 

expenditures for the 3- or 4-digit 

NAICS code. Sum the individual 

KLEM expenditures to estimate the 

total national factor expenditure for 

the NAICS code.

2013 industry sales from Annual Survey of 

Manufactures = $75.4 billion

K: 2.8% x $75.4 billion = $2.1 billon

L: 13.3% x $75.4 billion = $10.0 billon

E: 1.7% x $75.4 billion = $1.3 billon

M: 31.1% x $75.4 billion =$23.5 billon

Total KLEM national factor expenditure

= $36.9 billion

Step 5—Calculate KLEM 

expenditure impacts and 

composite cost impact on 

national factor 

expenditures.

Calculate KLEM expenditure impacts 

using average percentage impacts 

from Step 3 and KLEM expenditures 

from Step 4. Sum together each 

KLEM expenditure impact from Step 

5 to estimate the composite cost 

impact.

K: +12.5% x $2.1 billon = +$265 million

L: -5% x $10.0 billon = -$502 million

E: +5% x $1.3 billon = +$63 million

M: -2.5% x $23.5 billon = -$586 million

Composite cost impact = -$760 million

Step 6—Calculate composite 

cost impact in percentage 

terms.

Divide the composite cost impact 

from Step 5 by the total national 

factor expenditure from Step 4.

Percentage composite cost impact

= -$760 million/$36.9 billion = -2.1%
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Table 4-3. Economic Impact Summary Table

Industry Data, 2013 Cost Impacts, Billions

Sales

Shop Floor 

KLEM 

National 

Factor 

Expenditure

K: 

Capital L: Labor

E: 

Energy

M: 

Materials Total

Percentage 

Impact

Total $2.13 

trillion $759 billion +$11.4 -$22.1 -$0.4 -$29.3 -$40.4 -5.3%

Aerospace $224 

billion $47 billion -$0.4 -$4.6 +$0.1 -$2.6 -$7.5 -16.0%

Automotive $542 

billion $190 billion +$9.6 -$8.8 $0.0 -$12.4 -$11.5 -6.1%

Electronics $133 

billion $30 billion +$1.0 -$1.0 $0.0 -$1.8 -$1.8 -5.8%

Metal 

products

$183 

billion $103 billion -$0.1 -$4.3 -$0.1 -$3.8 -$8.3 -8.1%

Process $849 

billion $273 billion +$0.8 -$3.0 -$0.4 -$8.7 -$11.3 -4.1%

Other 

discrete 

parts

$203 

billion $115 billion +$0.4 -$0.4 $0.0 $0.0 $0.0 0.0%

Table 4-3 also shows the cost impacts for several industry 

sectors. Despite the fact that robotics and automation are 

already highly adopted and deployed in the automotive sector, 

there is still a large potential for new cost savings in that 

sector. According to our estimates, the aerospace sector, the 

metal products sector, and the process sector also represent 

large potential cost savings from enhanced technology 

infrastructure. In percentage terms, the aerospace and metal 

product industry sectors represent the largest potential cost 
Section 4 

— Quantitative Results and Economic Impact Analysis 4-13

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savings compared with current national factor expenditures at 

16.0% and 8.1% reductions, respectively.45

It is interesting to note the differential impacts on the factors of 

production across the six industries. Specifically, impacts on 

capital costs vary across the industries. Capital costs are 

expected to increase substantially in the automotive sector and 

to a much lesser degree in the electronics and process sectors, 

whereas capital costs are expected to decrease slightly in 

aerospace and metal products. Given that responses represent 

net impacts on costs, the difference in directions could reflect 

that interviewees did not hold output and time constant.

Interviewees from the automotive sector could be interpreting 

enhanced technology infrastructure as increasing domestic 

output as U.S. industry becomes more competitive 

internationally. However, from the data we have collected, it is 

not possible to determine the underlying root causes of these 

different impacts.

4.4.1 National-Level End User Impact by Capability

Figure 4-6 shows the distribution of total KLEM impacts

apportioned to each of the six capabilities. The distribution of 

impacts reflects the importance scores provided by end users.

Intuitive interfaces, safe HRI, and sensing and perception all 

have impacts greater than $7 billion. These are followed by 

interoperability and modularity as well as modeling and 

simulation, with $6.6 billion and $6.3 billion in cost impacts,

respectively. Reflecting its lower overall importance score, 

objective, low-cost performance characterization has the 

smallest impacts of all capabilities with $5.4 billion.

A caveat to Figure 4-6 is that there are often strong 

complementarities or “interaction effects” between capabilities 

that we were not able to quantify. For example, modeling and 

simulation tools supporting risk assessment can enable safe 

HRI. Another example is that improved interoperability and 

modularity can enable enhanced sensing and perception as 

sensors, perception equipment, and algorithms are more easily

 

45 Appendix D contains a vignette of how robotics and automation 

could transform the aerospace product and parts industry.
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Figure 4-6. Total Cost Impact, by Capability (Millions of 2013 US$)

integrated with robotics technologies. To the degree that these 

complementarities are roughly proportional across the six 

capabilities, Figure 4-6 represents a reasonable approximation.

4.4.2 Other Impacts Not Quantified

End user impacts that have not been quantified in this study 

include impacts on production and product offerings.

Enhanced technology infrastructure can impact production 

through reduced time and/or cost required for workcell setup, 

reduced unplanned downtime, and reduced productdevelopment-to-production cycles. Likewise, enhanced 

technology infrastructure can impact end users’ product 

offering by improving the quality of existing products, 

increasing the amount of customization possible, and 

accelerating the introduction of new products.

We were unable to quantify impacts of this kind because of

limited quantitative information. However, the production 

impacts are indirectly included in the KLEM impact estimates.

Impacts on product offering are not included in this study,

which makes the estimates herein more conservative in nature.
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5 Safe Human

-Robot 

Interaction

Safe HRI is arguably the most important and potentially 

transformative of the six needed capabilities for robotics

technology. The development and adoption of safe human-

robot technology is inhibited by several barriers to innovation 

(sources of market failure) such as the broad scope of 

commercial applications that is beyond the reach of any 

individual firm, technical risk, market risk due to safety issues 

and cultural acceptance, and difficulty in bringing together 

component technologies that are necessary for various 

approaches to safe HRI (see Table1-2).46

NIST can play a role in enhancing infratechnologies by 

providing standardized risk assessment tools and test methods, 

creating taxonomies and promoting paradigms of safe HRI, and 

coordinating with major players that have influence on robot 

safety standards, among other things.

Finally, the advancement of other needed capabilities such as 

sensing and perception and simulation and modeling through 

NIST R&D may serve to augment safe HRI.

5.1 BARRIERS TO INNOVATION AND SOURCES 

OF MARKET FAILURE

Drawing from the discussion in Section 1.3 and the taxonomy 

of barriers to developing and adopting new technologies that 

bring about market failure outlined in Table1-2, the barriers 

most relevant for safe HRI are47

 

46 From a network externality perspective, this might be referred to as 

an indirect network effect (Garrell and Saloner, 1985). 47 The reader should note that the barriers to innovation are different 

for each of the capabilities discussed in the subsequent sections.
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 a scope of commercial applications broader than the 

market strategy of any one firm;

 the risk that R&D outcomes will be technically

insufficient (technical risk);

 the risk that R&D outcomes, although technically 

sufficient, will gain insufficient market acceptance to 

provide an acceptable return on investment (commercial 

or market risk); and

 difficulties in bringing together component technologies 

from different industry segments.

These barriers are manifested in a variety of ways, but overall,

they serve to inhibit the development and adoption of safe HRI 

technology. Safe HRI will have many commercial applications 

that extend across multiple sectors of the economy; thus, it is 

unlikely that any individual firm would be able to appropriate all 

of the commercial profits. Safe HRI is also characterized by a 

high degree of technical risk given that it is such a nascent area 

of research and that existing safety standards are not well 

understood.

Market risk may be the greatest barrier inhibiting the 

development and adoption of safe HRI. Market risk includes 

several dimensions, but perhaps the most important is the 

cultural acceptance of robots. Many robot suppliers and end 

users cited this as a challenge. Additionally, to the extent that 

the United States and global regulatory frameworks reflect 

cultural values and norms that embrace the status quo, 

regulations may inhibit development and adoption of robotics 

technology. Safety standards, when adequate, will influence the 

adoption patterns of manufacturers by providing greater 

confidence and thereby increase market demand.

Finally, safe HRI will require the seamless integration of many 

component technologies such as robot arms, end effectors, 

vision systems, sensors, robot controllers, and other software. 

Because of a lack of interoperability and modularity in the 

industry (see Section 8), it is difficult to bring together multiple 

component technologies efficiently without a substantial 

investment of engineering resources.
Section 5 

— Safe Human-Robot Interaction 5-3

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5.2 TECHNOLOGY INFRASTRUCTURE NEEDS 

AND POTENTIAL ROLES FOR NIST More than 70% of interviewees assigned safe HRI an 

importance score of 4 or 5, indicating that this was a highly 

important capability. In fact, this score was higher than any 

other capability about which we asked (see also Section 3.3 for 

a comparison of score across the six capabilities). Not 

surprisingly, there were many substantive comments on how 

NIST could play a role in this technical area. Additionally, 

developers and end users agreed that this was a highly 

important need.48

Infratechnology needs have been discussed throughout the 

report, but Table 5-1 summarizes potential roles for NIST, 

ordered by frequency of occurrence. Of all interviewees, 48% 

provided at least one potential role for NIST in this area.

5.3.1 Risk Assessment and Measurement Tools

Although the RIA has promulgated safety standards and 

technical guidance, the interviewees with whom we spoke think 

that additional work can be done on safety standards to 

empower manufacturers to apply them. Interviewees stated 

that it is difficult to implement the standards even with the 

added benefit of injury data in the new ISO technical 

specification document. Interviewees suggested that NIST can 

contribute significantly to the continued development of safety 

standards and practices by developing and standardizing 

metrics and test protocols for measuring safety outside of a 

cage. For example, standardized metrics are needed for 

measuring pain, force, torque, and risk. Some work is being 

done in this area already, but NIST can contribute.

As a suite of metrics and test protocols starts to emerge, 

manufacturers require tools for performing streamlined risk 

assessments that reduce the need for integrators. The cost of 

integration can often act as a barrier to adoption because the 

up-front costs associated with integration are high. Thus, 

streamlined tools for risk assessment in collaborative robot 

applications involving safe HRI could accelerate the adoption of 

robots.

 

48 End users slightly preferred intuitive interfaces.
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Category Identified Needs

Infratechnologies Supporting standards development (crosscutting)

Taxonomy and paradigms for HRI

Risk assessment and measurement tools

Testbeds, test protocols, and test procedures

Guidelines and best practices

Technology platforms Sensing and perception for HRI

Simulation for HRI

Other Coordination with other organizations

Outreach

Note: Some interviewees provided multidimensional recommendations that 

were placed under more than one category.

RTI found, based on evidence from interviews, that the cost of 

integration, specifically the uncertainty over costs, could act as 

a barrier to adoption for many SMEs, thus creating the need for 

infratechnology. Standardized risk assessment tools could help 

drive down these costs. One developer’s view supports the 

issue of scalability and integration costs that could hinder the 

application of safe HRI: “We want collaborative robots to scale 

in the same way [traditional robots] scaled in the automotive 

industry. [Collaborative robots] cannot only be deployed by 

skilled engineers, cannot have safety companies come in for 

every deployment. No one knows the costs. Companies are not 

all carefully doing the risk assessment. The new technical 

specification that is getting formulized right now, my 

assessment is that a $30,000 to $40,000 robot with proper risk 

assessment rather than an ad hoc assessment would easily cost 

three to four times the initial cost.”

Also, as an automotive manufacturer pointed out, risk 

assessment tools, specifically standard test protocols for 

sensing forces, could be relevant to company testbeds.

5.3.2 Taxonomies and Paradigms for Human-Robot Interaction

Given that collaborative robotics is a relatively new concept, 

there is a steep learning curve for anyone who is beginning to 

learn about it. Potential end users are still figuring out what 

collaborative robotics means and how it can benefit their 

business. Although industry and government are working on 

educating individuals and developing common taxonomies and 

Table 5-1. Technology 

Infrastructure Needs 

Related to Safe HRI
Section 5 

— Safe Human-Robot Interaction 5-5

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definitions, collaborative robotics needs to be “right-sized,” as 

one developer mentioned, for appropriate situations. Firms with 

little to no exposure to robotics require assistance in 

understanding when collaborative robots would be 

advantageous or when traditional industrial robotics might be 

more appropriate. One developer pointed out that if an end 

user is performing high-speed operations, it may be appropriate 

to install a safety cage and use traditional industrial robots. 

NIST could document which classes of tasks (and associated 

ways of measuring these tasks) would be more suited for 

traditional industrial robots or collaborative robots.

Another interviewee pointed out that paradigms for HRI need to

be more realistic, alluding to the fact that paradigms that are 

being communicated are not as pragmatic as they should be.

Best practices and guidelines would be valuable for industry as 

it considers implementing collaborative robots and safe HRI. An 

integrator summed up the need: “Guidelines, best practices, 

and more concrete implementation information around the 

[safety] standards would be extremely helpful for industry. It would help us move more quickly.”

5.3.3 Coordination with Other Groups Such as the Robotic 

Industries Association

Several interviewees emphasized NIST as a credible, objective, 

unbiased third party. In relation to the process of setting and 

promulgating safety standards, a developer of collaborative 

robots remarked, “NIST is good at putting different players 

together. This could be a good place for NIST to establish some 

common expected behaviors. Leave it open to industry on how 

to communicate, but NIST can help with what to communicate.”

Several interviewees involved or familiar with the RIA safety 

standards process noted that NIST is involved in that process. 

However, it was noted that additional resources to support 

NIST’s efforts could be beneficial.

5.3.4 Other Potential Roles

A few interviewees suggested that safe HRI can be more rapidly 

applied and adopted if NIST were working on other technical 

capabilities such as sensing and perception and/or simulation 

capabilities. For example, one observer thought that offline 

simulation to model safe HRI would be beneficial for end users.
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Sensing and 

Perception for 

Unstructured 

Environments

Sensing and perception for unstructured environments is one of 

the most important capabilities because it would allow robots to 

navigate unstructured and/or semi-structured environments, 

which would enable new applications and more flexible 

deployment of robots in the factory environment. Improved 

sensing and perception would also directly support safe HRI.

The development and adoption of sensing and perception 

technology are inhibited by several barriers to innovation 

(sources of market failure) such as the difficulty in bringing 

component technologies together, the scope of commercial 

applications being broader than the market strategy of any one 

firm, and the long and uncertain lag between R&D investments 

and returns.

There appears to be a potential role for the public sector, 

perhaps through NIST, to play a role in enhancing 

infratechnologies by conducting research on new sensing 

technologies, improving interoperability in support of sensing 

and perception, working on market demonstration efforts. RTI 

also offers the idea of closer coordination and collaboration with 

other organizations as a vehicle for lessening innovation 

barriers, based on many comments from interviewees.
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6.1 BARRIERS TO INNOVATION AND SOURCES 

OF MARKET FAILURE

The barriers to innovation most relevant for sensing and 

perception are

 difficulties in bringing together component technologies 

from different industry segments,

 the long and uncertain lag between R&D investments 

and returns, and

 a scope of commercial applications broader than the 

market strategy of any one firm.

Sensing and monitoring capabilities that are needed will require 

advanced systems that tie together optic and tactile sensors 

with algorithms and robot controllers. Some approaches to 

sensing and monitoring require large amounts of computing 

power and thus will need to connect with the cloud-based 

computing networks. Bringing together these component 

technologies is difficult because of a general lack of 

interoperability.

Some approaches to sensing and perception will likely require 

years of technology development with no guarantee that this 

particular technology will gain widespread adoption. These long 

and uncertain lags between R&D investments and financial 

returns are a barrier to innovation. Approaches to sensing and 

perception that have shorter development horizons are likely to 

receive the most attention by developers in the private sector.

Additionally, as is true of many of the capabilities listed in 

Table 3-2, the scope of commercial applications for sensing and 

perception technologies is arguably greater than the market 

strategy of any particular firm, which leads to an 

underinvestment of private R&D.

Together, these barriers serve to inhibit the development and 

adoption of sensing and perception technologies that enable 

greater robot autonomy in unstructured and semi-structured 

environments.
Section 6 — Sensing and Perception for Unstructured Environments

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6.2 TECHNOLOGY INFRASTRUCTURE NEEDS 

AND POTENTIAL ROLES FOR NIST

Sixty-eight percent of interviewees gave sensing and perception 

an importance score of 4 or 5, which was the next highest 

percentage behind safe HRI. There were no notable differences 

in the average importance scores based on position in the value 

chain, which indicates broad agreement that this is a technical 

area worth NIST’s attention. The specific needs in this area are 

much more dispersed, yet common themes emerge (see 

Table 6-1). More than one-third of interviewees identified at 

least one need where NIST might be able to play a role in this 

area.

6.2.1 New Technologies for Sensing and Perception

In several technical areas, interviewees suggested a potential 

role for NIST in supporting the development of new and 

improved technologies. Improved robot mobility, navigation 

methods, machine vision methods, and methods for object 

identification were all mentioned as areas requiring additional 

attention. Although technology development per se is generally 

outside NIST’s mission scope, NIST could help develop and 

enable infratechnologies in these areas.

Category Identified Needs

Infratechnologies Supporting standards development (crosscutting)

Measuring system performance

Interoperability

Reference dataset

Sensor registration and calibration

Testbeds

Technology 

platforms

New technologies for sensing and perception

Improved robot mobility

Improved navigation methods

Machine vision methods

Methods for object identification

Tactile sensing and perception

Other Market demonstration

Collaboration with other organizations

Note: Some interviewees provided multidimensional recommendations that 

were placed under more than one category.

Table 6-1. Technology 

Infrastructure Needs 

Related to Sensing and 

Perception for 

Unstructured (or LessStructured) 

Environments
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An observer from the venture capital industry pointed out that 

NIST or other public sector entities could participate in 

navigation research, both the navigation algorithms and how 

robot arms can be combined with mobile platforms. Companies 

such as Adept Technology have navigation and obstacle 

avoidance capabilities, but according to the interviewee, these 

capabilities are somewhat rudimentary.

It was also suggested that the robots need to be able to 

distinguish objects from one another. A particularly interesting 

approach to object recognition described by a collaborative 

robotics manufacturer involved very little sophisticated machine 

vision at all. The idea involved having large cloud-based 

libraries of objects that correspond to object tags or barcodes. 

All a robot needs to do is scan the tag or barcode, or become 

aware of the object’s unique identifier through wireless sensing

so that the robot knows what the object is and all of its 

associated features.

It also appears that there is demand for sensors that can 

operate in extreme environments subject to temperature 

changes, high pressure, high speeds, or difficult surfaces. For 

example, a machine vision expert mentioned a need for autofocusing optical methods that can handle objects moving at 

high speeds by focusing in milliseconds and that are robust to 

rapid temperature changes. These types of auto-focusing 

methods would be useful generally for high-volume inspections.

In terms of the tactile end of the sensing and perception 

spectrum, there may be a role for NIST to undertake R&D 

related to dexterity, although one interviewee pointed out that 

this may be more of a long-term research project. A 

manufacturer of end effectors stated that although sensor 

registration and automated calibration procedures are state of

the art, NIST’s research on performance-benchmarking tools 

and metrics will help manufacturers assess whether a given 

robot has the requisite fine motor skills needed for certain tasks 

in a factory (NIST, 2015). However, not all interviewees 

agreed. Another interviewee emphasized that performancebenchmarking tools for dexterity may be useful in the long 

term, but in the short term, NIST could work more closely with 

industry partners to identify more immediate needs for sensing 

and perception performance benchmarking.
Section 6 

— Sensing and Perception for Unstructured Environments 6-5

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Interoperability

Several interviewees drew a direct connection between 

improved interoperability and improved sensing and perception. 

It was noted that improved interoperability will increase 

innovation in sensing and perception because the commercial 

incentives will be greater. Furthermore, the lack of 

interoperability increases the labor costs associated with 

implementing enhanced sensing and perception. One 

interviewee described this need as“Standardized software 

drivers and communications protocols would be something

NIST could help with. Most of the costs are labor hours. 

[Examples are] standards for USB and EB9 connectors.” See 

Section 8 for additional details about interoperability needs.

Market Demonstration

NIST has a role to play in demonstrating the application of new 

sensing and perception technologies. A developer noted that 

there is no “big picture approach of how robots would work with 

people.” Another integrator/user of robotics said that NIST 

could put implementation strategies in the public domain. A 

major roadblock to market demonstration is finding willing 

collaborators in the private sector. Companies may be hesitant 

to do this because they risk the loss of intellectual property. 

Perhaps NIST can find market demonstration mechanisms that 

diminish the risks and hesitations on the part of companies 

while providing some relevant information to other 

manufacturers.

Collaboration with Other Organizations

Interviewees reiterated the need for NIST to work closely with 

industry associations such as the RIA and other organizations 

to alleviate barriers to innovation. However, specific means of 

collaboration were not spelled out.

Other Potential Roles

A large automotive manufacturer familiar with NIST suggested 

that NIST become involved in developing standards around 

measuring systems performance of vision-guided robotic 

systems. Another developer pointed out that measuring and 

understanding performance at a granular product level are

currently well understood, but they fall apart at the systems 

level: “Validating sensors, calibration, performance benchmarks 

on an individual product level is easy, but as soon as it 
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becomes part of a system, how it functions, how data is 

utilized, becomes difficult to program and tie together.”

Yet, even at the individual product level, there are still apparent 

needs. Sensor registration and calibration, including selfcalibration, were mentioned by a metal product manufacturer 

who made products with very small tolerances.

Interviewees also identified other roles for NIST such as 

providing testbeds, generating reference datasets, and 

contributing to standardization. In relation to some of the other 

technical areas, several interviewees pointed out that sensing 

and perception on the whole is more of a long-term research 

topic for NIST than one that will have immediate payoffs. One 

specific long-term research topic that was mentioned is 

knowledge representation and reasoning (KR&R). One R&D 

expert from the automotive industry stated that “KR&R is more 

academic” in nature. Although some aspects of KR&R can have 

a more immediate impact, industry appears to view it as a 

more long-term research topic.
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7 Intuitive Interfaces

Intuitive interfaces for interacting with robots is an important 

industry need, especially considering the lack of skilled workers 

and technical knowledge that many companies described as a 

barrier to adoption.

Intuitive methods of programming and teaching robots such as 

GUIs would make robots more accessible to all manufacturers, 

specifically SMEs, which tend to have a smaller pool of technical 

knowledge to tap into.

The development and adoption of intuitive interfaces is 

inhibited by several barriers to innovation (sources of market 

failure) such as the risk that R&D outcomes, while technically 

sufficient, will gain insufficient market acceptance and 

difficulties in bringing together component technologies.

NIST can accelerate the development and adoption of intuitive 

interfaces by playing a direct role in their development. 

Comparisons were drawn between the need for intuitive robot 

interfaces and other languages such as SQL and M-code.49

These comparisons may indicate that although a GUI would be 

beneficial, the most important aspect of intuitive interfaces may 

simply be standardizing robot programming across the 

industry. This would prevent individuals from having to learn 

multiple robot programming languages and would allow a set of 

work instructions to be executed by any robot regardless of 

supplier.

Other industry initiatives toward this end, such as ROS and 

ROS-I, are under way. It is important that NIST research 

 

49

M-code is a subset of machine functions in G-code, a commonly 

used numerical control programming language.
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activities dovetail and complement existing efforts. NIST is 

working with this effort and is using ROS-I in ongoing projects.

7.1 BARRIERS TO INNOVATION AND SOURCES 

OF MARKET FAILURE

The barriers inhibiting the development and adoption of 

intuitive interfaces are

 commercial or market risk—the risk that R&D outcomes, 

although technically sufficient, will not be received well 

by the market, thereby providing an unacceptable return 

on investment; and

 difficulties in bringing together component technologies

(i.e., interoperability).

Market risk, brought about by market structure, may be the 

greatest barrier inhibiting the development and adoption of 

intuitive interfaces. Although there appears to be demand for 

intuitive interfaces by end users, major robot suppliers may 

prefer to maintain the status quo for robot programming—each 

robot supplier having its own proprietary robot programming 

language—because it limits customers’ ability to easily switch to 

other vendors. As systems become more standardized, robot 

suppliers may fear the loss of customer and other competitive 

advantages.

Although some major robot suppliers appear to be moving 

toward more open robot systems, developers of intuitive robot 

interfaces may produce a technology that does not gain traction 

with robot suppliers. This market risk inhibits investment in 

R&D.

Difficulties bringing together component technologies are 

another barrier for developing intuitive interfaces. There is a 

plethora of traditional industrial robots and collaborative robot 

suppliers. Being able to develop an intuitive robot interface that 

works across all robots requires an in-depth understanding of 

all of these products and developing translators that can 

communicate between the intuitive interfaces and each of the 

proprietary systems. One interviewee suggested that the 

programmable logic controllers (PLCs) may be part of the 

solution to this problem.
Section 7 — Intuitive Interfaces

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7.2 TECHNOLOGY INFRASTRUCTURE NEEDS 

AND POTENTIAL ROLES FOR NIST

Sixty-five percent of interviewees gave intuitive interfaces an 

importance score of 4 or 5. End users attributed a much higher 

importance score to intuitive interfaces than developers. In 

fact, end users rated intuitive interfaces the most important of 

the six technical areas. Although end users generally placed a 

high importance on intuitive interfaces, the specific needs in 

this area are much more dispersed, and only 20% of all 

interviewees provided a specific potential role for NIST, 

indicating that there may be a lack of clarity around what the 

appropriate role for NIST would be to complement existing 

industrial activities (see Table 7-1).

7.2.1 Standardized Robot Programming

Several interviewees noted that the lack of ease of use of 

robots can be a barrier to adoption—specifically, the ability to 

re-task and re-train robots as needed rather than installing a 

robot for a particular task for a set number of years, as has 

been the case in the automotive sector. Other studies have 

noted user friendliness as a barrier to adoption as well

(PricewaterhouseCoopers, 2014). Companies such as Rethink 

Robotics and Universal Robots have made efforts to simplify the 

user experience with their robots that are branded as 

collaborative.

Category Identified Needs

Infratechnologies Technology specifications

Assess software systems design

Interoperability with equipment

Standard libraries and methodologies

Technology 

platforms

Mobility, monitor robot status on a device

Standardized robot programming

Note: Some interviewees provided multidimensional recommendations that 

were placed under more than one category.

Table 7-1. Technology 

Infrastructure Needs 

Related to Intuitive 

Interfaces
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To enable more flexible uses of robotics for factories with a 

more diverse product mix, several interviewees noted that NIST 

could contribute to developing a more standardized, generic 

programming interface. One aerospace manufacturer stated 

that “because it links with sensing and perception, ROS helps 

but much up front work is still needed.”

An electronics manufacturer stated that a simple GUI would be 

ideal.

An automotive R&D expert suggested that a standard 

programming interface would need to be able to communicate 

with each robot controller by understanding the internal 

programming and protocols to perform the necessary 

conversion. A developer drew a comparison between a 

standardized methodology for programming robots and M-code,

a machine tool standard that generalizes work instructions for 

computer numerical control systems and other machines.

7.2.2 Other Potential Roles

A few interviewees pointed out other infratechnology needs

including developing standard libraries to support existing robot 

interfaces, assessing robot software systems design, and 

contributing to infratechnologies that support monitoring the 

status of a robot via mobile device.
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8 Interoperability and 

Modularity

Interoperability and modularity, although not the most 

important capability in and of itself, can support other

capabilities such as sensing and perception and intuitive 

interfaces. Improved interoperability and modularity of robotics 

technology alleviates the difficulty of bringing together 

component technologies, which is a common barrier to 

innovation in this industry.

Plug-and-play interoperability can be achieved by standardizing

physical interfaces, electronic interfaces, and software 

interfaces, or translators.

Interoperability and modularity is inhibited by several barriers 

to innovation (sources of market failure) such as positive 

network externalities, difficulties in bringing together 

component technologies such as physical interfaces for 

hardware or communications protocols for software, and 

industry structure.

NIST can accelerate the development and adoption of intuitive 

interfaces by conducting research that supports 

standardization. NIST can also emphasize the importance of 

coordination among robot manufacturers, end users, industry 

associations, and professional groups. Horizontal 

interoperability and vertical interoperability are needed.

8.1 BARRIERS TO INNOVATION AND SOURCES 

OF MARKET FAILURE The inability to appropriate all social benefits, such as positive 

network externalities, is the main barrier that inhibits the 

development of more interoperable and modular robotics 

technology. Improved interoperability and modularity of 
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robotics technology would alleviate the difficulty of bringing 

together component technologies, which is a common barrier to 

innovation that spans the needed capabilities (see Sections 5.1, 

6.1, and 7.1). These broad-based benefits are difficult for any 

single firm to appropriate.

8.2 TECHNOLOGY INFRASTRUCTURE NEEDS 

AND POTENTIAL ROLES FOR NIST

Fifty-eight percent of interviewees assigned interoperability and 

modularity an importance score of 4 or 5, which was the fourth 

highest percentage compared to all of the capabilities listed in 

Table 3-2.50 The average scores for developers and end users 

were almost identical. Of all interviewees, 32% provided a 

specific potential role for NIST. Most comments centered on

interoperability with robot components and peripherals, 

interoperability with other systems and machines, coordinating 

with other organizations, and enabling interoperability through 

standards (see Table 8-1).

8.2.1 Interoperability with Components and Peripherals

(Horizontal Interoperability)

The infratechnology needs voiced by interviewees for 

interoperability with components and peripherals include end

effectors, tooling, robot arms, vision systems, and safety 

systems. Those needs mentioned ranged from communication 

standards, physical connection and interface standards, and 

operating system standards.

One industrial automation developer said efforts have been 

made in these areas, but he had “doubt that anything has 

crossed [robot] platforms.” A collaborative robot developer said

industry-wide standards would help the industry grow more 

rapidly because proprietary standards and custom, integrated 

solutions have actually constrained the industry’s ability to 

expand. Overall, the industry appears to be a long way from 

plug-and-play interoperability. One developer said he wanted 

“physical interfaces and physical behavior defined. If you can 

specify an interface between a robot and a safety monitoring 

system, you reduce the need for engineering degrees.”

 

50 See Figure 3-5 for a comparison of scores.
Section 8 — Interoperability and Modularity

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Category Identified Needs

Infratechnologies Interoperability with components, 

peripherals

Interoperability with other systems

Standards development

Integration with programming

Technology platforms Characterize factory environments

Other Adoption of standards

Note: Some interviewees provided multidimensional recommendations that 

were placed under more than one category.

8.2.2 Interoperability with Other Systems (Vertical 

Interoperability)

Plug-and-play interoperability allows end users to choose their 

preferred robot components, software, and vision systems. 

Plug-and-play interoperability enables the robotic system to 

“talk” with other factory systems and machines, which would 

entail additional benefits.

Manufacturing enterprise solutions vary from plant to plant and 

company to company. One developer suggested that NIST 

could help define standards for warehouse systems, specifically 

standards for instructions to robots on when to execute a task 

or subset of tasks. NIST could help define standards for 

communications back to the manufacturing execution system 

regarding what has been completed, cycle times, performance 

metrics, and other granular data. Part of the interoperability 

challenge is clarity around protocols for combining wired and 

wireless communications in ways that do not compromise 

security.

8.2.3 Standards

Although many interviewees believe that standards are needed, 

part of the challenge may be coordinating and persuading big 

players to adopt existing standards. One machine vision 

developer pointed out that a barrier to interoperability is not 

the lack of standards and protocols, but rather the lack of 

adoption of the standards and protocols by the big players in 

robotics manufacturing. One interviewee suggested a possible 

role for NIST or other public sector bodies to facilitate

coordination among robot manufacturers, end users, industry 

associations such as Semiconductor Equipment and Materials 

International, and professional groups such as IEEE.

Table 8-1. Technology 

Infrastructure Needs 

Related to

Interoperability and 

Modularity
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9 Modeling and 

Simulation

Modeling and simulation appeared to be somewhat less 

important relative to some of the other capabilities. However, 

modeling and simulation tools for risk assessments could 

enable the more efficient deployment of collaborative robots.

Simulation could significantly reduce the time and cost involved 

in changing between production lines if the factory floor was 

software reconfigurable. Other benefits of simulation include a 

better understanding of tool paths and documenting as-built 

products compared with their digital counterparts. Simulation 

models could also be used to convert robot training and 

teaching on the factory floor into transferable knowledge that 

can be applied to different situations in the future.

Modeling and simulation is inhibited by two barriers to 

innovation (sources of market failure): technical risk and the 

difficulty of bringing together component technologies.

The most important future application of simulation appears to 

be for supporting safe HRI through risk assessment and 

quantification of relevant dimensions of HRI. Key 

infratechnologies that NIST can provide include libraries, 

reference data, and reference models. There are also some 

industry needs around measuring the fidelity of simulation 

models.

9.1 BARRIERS TO INNOVATION AND SOURCES

OF MARKET FAILURE

Technical risk and difficulties in bringing together component 

technologies act as barriers to innovation that inhibit the 

development of modeling and simulation technology. With 

advanced simulation models, there is a risk that R&D will yield 
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insufficient technical outcomes. It is extremely challenging to 

develop a simulation model that adequately captures the role 

that robots play in a variety of manufacturing industries with 

widely different production processes. Furthermore, different 

levels of fidelity are needed for different situations, and a onesize-fits-all approach to simulation may not suffice.

As with the other capabilities, the lack of interoperability of 

component technologies makes simulation more difficult 

because it is not simple to model a digital expectation of how a 

patchwork of component technologies will work together as a 

system.

9.2 TECHNOLOGY INFRASTRUCTURE NEEDS 

AND POTENTIAL ROLES FOR NIST

Forty percent of interviewees gave modeling and simulation an 

importance score of 4 or 5. Users attributed slightly more 

importance to modeling and simulation than developers. Only 

23% of all interviewees provided potential roles for how NIST 

can contribute in this area (see Table 9-1).

The most important future application of simulation appears to 

be for supporting safe HRI through risk assessment and 

quantification. Key infratechnologies for simulation include 

libraries, reference data, and reference models. Another 

measurement issue is the level of fidelity achieved when 

combining different robots with different simulation systems.

Finally, a longer-term research topic would be how to use 

simulation for virtual testing of machine vision systems. 

Another noteworthy idea was creating a computer-aided design

import-robot output tool similar to the instructions that are 

used for CNC machines.

It is important to note that although modeling and simulation 

capabilities are somewhat less important for robotics, they are

highly relevant for smart manufacturing as discussed in one of

the companion report to this one titled Economic Analysis of 

Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing Processes.
Section 9 — Modeling and Simulation

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Category Identified Needs

Infratechnologies Data for simulation, libraries

Machine vision, virtual testing

Measure for fidelity of robot-simulation 

combination

Reference models

Technology 

platforms

Computer-aided design import tool, robot 

output tool

Simulation for safe HRI

Note: Some interviewees provided multidimensional recommendations that 

were placed under more than one category.

Table 9-1. Technology 

Infrastructure Needs 

Related to Modeling and 

Simulation
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10

Objective, Low-Cost 

Performance 

Characterization

Objective, low-cost performance characterization would address 

informational asymmetries between developers and end users 

of robots. The importance of this capability depends on the 

unique context and situations of each end user.

If objective, low-cost performance characterization were to be 

standardized, it could stimulate innovation by giving users a 

common way to communicate needs to developers and by 

giving developers a common target for focusing R&D efforts. 

However, interviewees suggested that the role for NIST might 

be limited here because industry would need to lead any 

measurement standards. Generally, industry has to be involved 

in developing standards that it will use, but the importance of 

industry involvement is particularly strong in this case because 

performance measures are directly used to market robotics 

technologies.

Perhaps the single greatest need for performance 

characterization would be in the area of measurement 

supporting safe HRI.

10.1 BARRIERS TO INNOVATION AND SOURCES 

OF MARKET FAILURE

Several barriers have limited objective, low-cost performance 

characterization, but the most prominent is informational 

asymmetries between developers and end users of robotics 

technology. Objective, low-cost performance characterization 

would help address these informational asymmetries.
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Objective data would benefit end users and developers by 

lessening transaction costs involved in overcoming common 

informational asymmetries. In the absence of such data, end 

users find it costly to convey their needs to developers, and 

developers similarly find it costly to communicate to the end 

users what their systems can and cannot do and to prove that 

their systems will perform as advertised. Furthermore, there 

may be situations where end users do not realize that they are 

paying for capabilities that are not optimal for their particular 

applications. As one developer of robot end effectors noted, 

“We’ve come across smaller customers where they need to buy 

a robot for a particular task, [and] they are sometimes misled 

into buying 6 axis robots, but they only need a faster 2 axis 

robot. Being able to have standard ways of communicating that 

would be important for our customers.”

Common metrics for evaluating robotic systems are not only 

important for end users of robots, but common metrics can also 

give robot developers a common target for focusing R&D 

efforts, which could serve to stimulate innovation.

10.2 TECHNOLOGY INFRASTRUCTURE NEEDS 

AND POTENTIAL ROLES FOR NIST

Developers and end users agreed that this capability was the 

least important of the six that we outlined in our interview 

guide. Only 30% of interviewees gave this an importance of 4 

or 5. It appears that objective, low-cost performance 

characterization would be something nice to have, but other 

capabilities are more clearly more pressing. Many interviewees 

did not perceive this to be an issue at all.

A few individuals were outspoken regarding their belief that this 

is an important issue. A machine vision developer (and others) 

suggested that robot vendors are not transparent in how they 

measure repeatability. One developer went as far as to say that 

if performance characterization is done right, it will stimulate 

innovation.

Others gave a more nuanced explanation, explaining that 

performance characterization is relevant to only the very 

sophisticated end users. A robotics gripper manufacturer 

stated, “Performance characterization is important to upper 

echelon of manufacturing—automotive and others that run 3 

shifts a day.”
Section 10 

— Objective Low-Cost Performance Characterization

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There are examples of where technology developers can 

improve performance characterization and make it more 

transparent, but most people believe that current market conditions are sufficient and any standardization in how 

developers define and measure performance of their products—

ultimately, how developers market their products to 

customers—would need to be industry driven. Perhaps the 

single greatest area of performance characterization would be 

in the area of measurement supporting safe HRI (see 

Section5).
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11 Conclusion

The deployment of robots in the U.S. manufacturing sector has 

largely been limited to automotive manufacturing. If critical 

enabling infratechnologies were in place, advanced capabilities, 

such as safe HRI and enhanced sensing and perception, would 

be possible. The existence of advanced capabilities (and 

associated infratechnologies), as outlined in this report, would 

stimulate industry investment in robotics technology and lead 

to the realization of an estimated $40.4 billion in net economic 

savings of U.S. manufacturers.51

As robots become more user friendly and easier to integrate 

with existing production lines, SMEs, which have limited 

adoption of robotics, would be able to implement robotics and 

automation technologies on a broader scale. Increased adoption 

of robots in the U.S. manufacturing sector would reduce labor 

costs, increase productivity, and improve product quality. These 

economic impacts would support the policy goal of improving 

the international competitiveness of the U.S. manufacturing 

sector.

Several barriers to innovation lead to a shortage of critical 

infratechnologies and inhibit the development and adoption of 

robotics technology. The rate and extent of development of 

robotics technologies with the needed capabilities and the rate 

and extent of their adoption in advanced manufacturing 

applications will depend on the parallel development and 

diffusion of technology infrastructures that are generally 

underprovided by the market. This resulting market failure—the 

failure of the market to allocate a socially optimal level of 

 

51 We believe this to be a conservative estimate of net economic 

savings for the variety of reasons detailed in Section 2.
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infrastructure—provides an opportunity to improve the 

efficiency of economic outcomes through public investments in 

technology infrastructure. NIST can accelerate the realization of 

the economic benefits of robotics technology by providing and 

contributing to infratechnology. Industry experts throughout 

the robotics and automation fields have recommended a variety 

of roles for NIST to support capabilities by providing 

infratechnologies.

11.1 CAPABILITIES AND INFRATECHNOLOGY 

NEEDS

Interviewees indicated the importance of a broad range of 

capabilities for robotics and automation (see Figure 11-1).

If we interpret important as a 4 or a 5 and not important as a 

1, 2, or 3, the most important capabilities ranged from safe 

HRI; to sensing and perception for unstructured or semistructured environments; to interoperability and modularity of 

robots with components, peripheral devices, and other 

systems; and to intuitive interfaces for robot programming.

Figure 11-1. Importance of Capabilities/Needs and Corresponding Infratechnology Needs, 

Diverging Stacked Bar Chart
Section 11 — Conclusion

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In addition to the implicit hierarchy of importance illustrated in 

Figure 11-1, there are significant interdependencies between 

the capabilities. For example, although objective, low-cost 

performance characterization received the lowest average 

importance score, technology infrastructure that supports 

objective, low-cost performance characterization can make it 

more feasible to implement safe HRI. Additionally, modeling

and simulation tools can support safe HRI. Furthermore, 

improved interoperability and modularity could stimulate 

innovation in sensing and perception by broadening the 

potential market size for new sensing technologies.

Notwithstanding these interdependences, the level of 

importance of these capabilities might be viewed as a secondorder indicator for the areas in which NIST could play a role in 

advancing the development and adoption of robotics technology

by providing infratechnologies.

11.2 ECONOMIC IMPACTS

Interviewees were asked about the economic impact that 

enhanced robotics and automation capabilities and associated 

infratechnologies could have on their manufacturing processes.

Table 11-1 shows the average percentage change in KLEM 

factor inputs provided from the interviews.

End users indicated that labor costs could be reduced by an 

average of 18%, and materials costs could be reduced by 8%. 

These cost savings could be achieved by a net increase in 

capital expenditures of 22%. The economic impact of the 

increased adoption of robotics and automation on energy 

consumption would be negligible.

Changes in factor inputs were then scaled to the national level 

to estimate the economic impacts associated with enhanced 

infratechnology. Table 11-2 shows that national economic 

impacts are estimated to be approximately $40.4 billion.

Factor Input

Mean Impact of Meeting

Industry Needs

K: Capital +22%

L: Labor −18%

E: Energy +1%

M: Materials −8%

Table 11-1. Percentage

Change in Factor Inputs

Due to Meeting Industry 

Needs 
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Table 11-2. Economic Impact Summary Table

Industry Data, 2013 Cost Impacts, Billions

Percentage 

Sales Impact

Shop Floor 

KLEM 

National 

Factor 

Expenditure

K: 

Capital

L: 

Labor

E: 

Energy

M: 

Materials Total

Total $2.13

trillion $759 billion +$11.4 −$22.1 −$0.4 −$29.3 −$40.4 −5.3%

Note: Industry detail is provided in Table 4-3.

The aggregate national economic impacts represent, on 

average, a 5% reduction in national factor expenditures.

Materials savings, although a smaller percentage impact than 

labor savings, accounts for the largest share of savings because 

it accounts for approximately three-fifths of manufacturing 

production costs.

11.3 TECHNOLOGY INFRASTRUCTURE NEEDS 

AND POTENTIAL ROLES FOR NIST

Industry experts provided specific recommendations regarding 

the role NIST could play to accelerate the development and 

adoption of robotics technology in the U.S. manufacturing 

sector. Interviewee recommendations provide a first-order 

indicator of where NIST could lead, coordinate, or encourage 

development in each of these areas. An overview of some of 

the most commonly cited recommended roles for NIST is 

provided below.

11.3.1 Risk Assessment and Measurement Tools for Safe 

Human-Robot Interaction

Although the RIA and ISO have promulgated safety standards 

and recent technical guidance, the interviewees with whom we 

spoke think that additional work can be done on safety 

standards to empower manufacturers to apply them. 

Interviewees stated that it is difficult to implement the 

standards even with the added benefit of injury data in the new 

ISO technical specification document. Interviewees suggested 

that NIST can contribute significantly to the continued 

development of safety standards and practices by developing 

and standardizing metrics and test protocols for measuring 
Section 11 

— Conclusion

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robot safety outside of a cage. For example, standardized 

metrics are needed for measuring pain, force, torque, and risk.

Some work is being done in this area already, but NIST can 

contribute.

As a suite of metrics and test protocols starts to form, NIST can 

provide streamlined tools for risk assessment in collaborative 

robot applications involving safe HRI. Risk assessment and 

measurement tools could accelerate the adoption of robots by 

making robots less costly to integrate with existing production 

lines.

11.3.2 New Technologies for Sensing and Perception Improved robot mobility, navigation methods, machine vision 

methods, dexterity, and methods for object identification were 

all mentioned as areas that require additional attention. There 

also appears to be demand for sensors that can operate in 

extreme environments subject to temperature changes, high

pressure, high speeds, or difficult surfaces.

Some of the needs for new sensing and perception technologies 

will likely have long-term horizons before research can be 

applied in the manufacturing sector.

A more near-term research activity for NIST to support object 

identification described by a collaborative robotics manufacturer 

involved NIST providing large cloud-based libraries of objects 

that correspond to object tags or barcodes, which can a robot 

can wirelessly sense or optically scan in order to recognize all of 

the object’s associated features.

11.3.3 Standardized, More Intuitive Robot Programming

The lack of ease of use of robots can be a barrier to adoption—

specifically, the ability to re-task and re-train robots as needed 

rather than installing a robot for a particular task for a set 

number of years, as has been the case in the automotive 

sector.

To enable more flexible uses of robotics for factories with a 

more diverse product mix, several interviewees noted that NIST 

could contribute to developing a more standardized, generic 

programming interface or GUI.

Comparisons were made to SQL and M-code. Any activities that 

NIST undertakes in this area will need to complement existing 

industry activities such as ROS and ROS-I.
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Robotics and Automation

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5

11.3.4 Interoperability

Improved horizontal interoperability—interoperability with 

components and peripherals including end effectors, tooling, 

robot arms, vision systems, and safety systems—and vertical 

interoperability—would serve to stimulate innovation and 

decrease the costs of installing and integrating robotics 

technology. Improved interoperability requires software, 

electronic, and physical hardware connection and interface 

standards.

Plug-and-play interoperability enables end users to choose their 

preferred robot components, software, and vision systems. 

Plug-and-play interoperability also enables the robotic system 

to “talk” with other factory systems and machines, which would 

entail additional benefits.

Additionally, there is a direct connection between improved 

interoperability and improved sensing and perception. 

Improved interoperability will increase innovation in sensing 

and perception because the commercial incentives will be 

greater. Furthermore, the lack of interoperability increases the 

labor costs associated with implementing enhanced sensing and 

perception.

11.3.5 Coordination with Other Organizations

NIST is a credible, objective, unbiased third party that can 

provide inputs to the standard setting process. However, 

additional resources to support NIST’s efforts could be 

beneficial. There is a need for NIST to work more closely with 

industry groups and companies across a variety of technical

areas to alleviate barriers to innovation.
Biblio

-1

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Appendix A:

Interview Guide for 

Robotics/Automation Systems 

Developers
Appendix A 

— Interview Guide for Robotics/Automation Systems Developers

A-1

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-005

NIST Advanced Manufacturing Strategic Planning Study

Interview Guide for Robotics/Automation Systems 

Developers

The National Institute of Standards and Technology (NIST) in 

the U.S. Department of Commerce has contracted with RTI 

International to conduct an economic analysis of standards, 

measurement, and general purpose technology needs that 

inhibit efficient development and adoption of advanced 

manufacturing in the United States.

The objectives of this critical strategic planning study are to  identify current and emerging needs related to 

standards and measurement,

 estimate the economic impact of meeting these needs, 

and

 review public policy and investment options.

The study has a particular focus on 4 aspects of advanced

manufacturing: (1) robotics and automation, (2) smart 

manufacturing processes, (3) 3D Printing (additive 

manufacturing), and (4) roll-to-roll manufacturing. The focus of 

our conversation is robotics and automation.

Your perspective will help guide NIST’s planning and investment 

process. Participation in this analysis is confidential; only 

aggregated information will be included in any deliverables or 

communications. Your name and your company’s name will not 

be disclosed. We do not wish to discuss specific products, 

strategies, or technologies; but rather your thoughts about how 

investments in standards and measurement technologies would 

affect your company and companies like yours.

Our research products will be an economic analysis, final 

report, and presentation materials. All deliverables will be 

publicly available in late 2015 and these will be shared with you 

as soon as they are released.

If you have questions, please contact:  Alan O’Connor, Principal Investigator, RTI, 919

-541-

8841 or oconnor@rti.org

 Gary Anderson, NIST Project Officer, NIST, 301-975-

5238 or gary.anderson@nist.gov

This collection of information contains Paperwork Reduction Act 

(PRA) requirements approved by the Office of Management and 
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: Advanced 

Robotics and Automation

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5

Budget (OMB). Notwithstanding any other provisions of the law, 

no person is required to respond to, nor shall any person be 

subject to a penalty for failure to comply with, a collection of 

information subject to the requirements of the PRA unless that 

collection of information displays a currently valid OMB control 

number. Public reporting burden for this collection is estimated 

to be 35 minutes per response, including the time for reviewing 

instructions, searching existing data sources, gathering and 

maintaining the data needed and completing and reviewing the 

collection of information. Send comments regarding this burden 

estimate or any aspect of this collection of information, 

including suggestions for reducing this burden, to the National 

Institute of Standards and Technology, Attn., Gary Anderson, 

gary.anderson@nist.gov, (301) 975-5238. The OMB Control 

Number is 0693-0033, with an expiration date of 03/31/2016.
Appendix A — Interview Guide for Robotics/Automation Systems Developers

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5

Name

Title

Company

Industry

Phone

Email

Location

Respondent Background

1. Please give a brief description of your experience with 

robotics and automation systems.

About Your Company

2. What types of robotics and automation systems does your 

company develop?

3. What are your customers’ primary lines of business (i.e., 

industry classification)? In other words, what are the 

manufacturing applications in which your systems are most 

commonly used?

Industry Needs

Several industry-level needs have been identified, and 

discussions with experts have suggested measurement and test 

methods, material and process standards, reference databases, 

and general purpose technologies that could meet these needs.

Please review the summary table on the next page, thinking 

about how these needs apply to your company’s development 

of robotics and automation technology.

Table A-1. Respondent 

Contact Information
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Robotics and Automation

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5

Table A-2. General Industry-Level Needs for Robotics and Automation

Industry Needs

Infrastructure Technology to Help 

Meet Needs

Rating of 

Importance 

(1 to 5)

5=Most

1=Least

Safe human-robot interactions

Universal standards for developers of 

robotic technologies and the application 

of these technologies in manufacturing 

settings with robots working in close 

proximity to people (see more below on 

sensing/perception for unstructured 

environments, relevant for intuitive HRI)

• Test protocols, objective scientific 

and engineering data, reference 

databases, and other technical 

inputs into standards for safe HRI

(power/force-limiting; 

speed/separation monitoring; 

hand-guided operation; safetyrated monitored stop)

Sensing and perception for 

unstructured (or less-structured) 

environments

Improved perception (and the ability to 

plan and re-plan its actions based on 

what it “sees” and “knows”) giving a 

robot greater autonomy, lessening its 

demand that its work environment meets 

stringent tolerances

• Sensor registration & calibration

• Performance characterization 

(benchmarks, testbeds, and 

technical inputs to standards to 

characterize the performance of 

systems, subsystems, and 

components)

• Sensing/perception 

engines/architectures

• Proof-of-concept robotics 

applications of KR&R (knowledge 

representation & reasoning)

Objective, low-cost performance 

characterization

Making it easier for robotics users to 

know what they are buying, easier for 

developers/suppliers to show what their 

systems do

• Common performance metrics, 

objective data, testbeds, test 

methods, and benchmarks to 

characterize the performance 

attributes of advanced systems, 

subsystems, and components

Interoperability and modularity

Plug-and-play for system components, 

enabled by standards for physical and 

electronic interfaces, software 

interfaces/translators

• Objective technical inputs into the 

standards-setting process: 

scientific and engineering data, 

benchmarks, testbeds, objective 

third-party testing of candidate 

technologies/configurations

Intuitive interfaces

Enabling rapid programming/training 

without specialized skills

• Protocols to simplify the 

programming, training, and rapid 

re-tasking of robots

• Standard programming language 

for industrial robotics analogous to 

SQL or HTML

Modeling and Simulation

Virtual factory floor allowing modeling 

and simulation, calibrated based on realtime data feed from robots, machine 

tools, sensors, control systems on the 

floor.

• Robust, open, real-time operating 

system on the factory floor

• Reference models, modeling 

frameworks to fully integrate 

robots into models of the 

manufacturing environment and

enable robust simulation/prediction
Appendix A — Interview Guide for Robotics/Automation Systems Developers

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5

4. Are any of the Industry Needs the focus of current R&D at 

your company? Which ones?

5. If one or more of the infrastructure technologies described 

were to become (more) available, would it benefit your 

company? If so, how?

6. Which items (both Needs and Infrastructure Technology) 

listed in the table are most relevant to your company? If 

several items are relevant, how would you prioritize them?

7. Are there items not included in the table that you would 

include? If so, what are they?

8. If the Infrastructure Technology described above was 

available today, how would that impact your company’s 

development and commercialization of robotics/automation 

systems?

Table A-3. Development and Commercialization

Impacts Description of Impact

+/- % Change, 

if applicable

a R&D costs? %

b R&D opportunities (likelihood of technical 

success)?

%

c Market opportunities? %

d Accelerated development of technologies 

improving the performance of existing 

products?

%

e Accelerated development of altogether 

new products?

%

f In other ways? %

9. Would you expect any changes in your company’s 

investment patterns or risk tolerance, if the Infrastructure 

Technology described above was available today? If so, 

what types of changes?

10. Could you briefly describe what could be expected in terms 

of new products or applications if this infrastructure 

technology was all available today? Would certain kinds of 

robots come to market sooner, or would robots be used in 

new ways sooner?
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Robotics and Automation

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5

11. To enable us to combine your responses with the responses 

of others and provide NIST with a sense of potential 

impacts at the industry level, could you quantify these 

impacts in terms of a relative change in your company’s 

sales?

12. Would you say that your answer to question 11 is 

representative of your industry (of companies 

developing/commercializing robotics/automation systems), 

or of only a subset? Please explain briefly how, if at all, the 

anticipated impacts for your company may be different 

from the industry as a whole, or how different industry 

segments may be differently impacted.

13. Are there any additional comments you would like to 

share?

Table A-4. Sales Sales %
This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-005

Appendix B:

Interview Guide for 

Robotics/Auto-

mation Systems 

Users
Appendix B 

— Interview Guide for Robotics/Automation Systems Users

B-1

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-005

NIST Advanced Manufacturing Strategic Planning Study

Interview Guide for Robotics/Automation Systems Users

The National Institute of Standards and Technology (NIST) in 

the U.S. Department of Commerce has contracted with RTI 

International to conduct an economic analysis of standards, 

measurement, and general purpose technology needs that 

inhibit efficient development and adoption of advanced 

manufacturing in the United States.

The objectives of this critical strategic planning study are to  identify current and emerging needs related to 

standards and measurement,

 estimate the economic impact of meeting these needs, 

and

 review public policy and investment options.

The study has a particular focus on 4 aspects of advanced 

manufacturing: (1) robotics and automation, (2) smart 

manufacturing processes, (3) 3D Printing (additive 

manufacturing), and (4) roll-to-roll manufacturing. The focus of 

our conversation is robotics and automation.

Your perspective will help guide NIST’s planning and investment 

process. Participation in this analysis is confidential; only 

aggregated information will be included in any deliverables or 

communications. Your name and your company’s name will not 

be disclosed. We do not wish to discuss specific products, 

strategies, or technologies; but rather your thoughts about how 

investments in standards and measurement technologies would 

affect your company and companies like yours.

Our research products will be an economic analysis, final 

report, and presentation materials. All deliverables will be 

publicly available in late 2015 and these will be shared with you 

as soon as they are released.

If you have questions, please contact:  <Case Lead or Interviewer Name>  Alan O’Connor, Principal Investigator, RTI, 919

-541-

8841 or oconnor@rti.org

 Gary Anderson, NIST Project Officer, NIST, 301-975-

5238 or gary.anderson@nist.gov
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: Advanced 

Robotics and Automation

B-2

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5

This collection of information contains Paperwork Reduction Act 

(PRA) requirements approved by the Office of Management and 

Budget (OMB). Notwithstanding any other provisions of the law, 

no person is required to respond to, nor shall any person be 

subject to a penalty for failure to comply with, a collection of 

information subject to the requirements of the PRA unless that 

collection of information displays a currently valid OMB control 

number. Public reporting burden for this collection is estimated 

to be 35 minutes per response, including the time for reviewing 

instructions, searching existing data sources, gathering and 

maintaining the data needed and completing and reviewing the 

collection of information. Send comments regarding this burden 

estimate or any aspect of this collection of information, 

including suggestions for reducing this burden, to the National 

Institute of Standards and Technology, Attn., Gary Anderson, 

gary.anderson@nist.gov, (301) 975-5238. The OMB Control 

Number is 0693-0033, with an expiration date of 03/31/2016.

Respondent Contact Information

1. Name

2. Title

3. Division

4. Company

5. Telephone

6. Email

7. Location, if not USA

Respondent Background

8. Please give a brief description of your experience with 

robotics and automation systems. How did you come to be

in your current position?

9. How familiar are you with the National Institute of 

Standards and Technology?

About Your Company

10. How would you describe your primary line of business (i.e., 

industry classification)? What do you produce and sell 

most?
Appendix B 

— Interview Guide for Robotics/Automation Systems Users

B-3

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a. Approximately what percentage of your company’s 

sales revenue is associated with your division, or the 

division for which you are responsible? A range is fine.

11. How does your company use robotics and automation 

systems?

a. For what manufacturing processes, or for the 

manufacture of what products?

b. What types of robotics and automation systems do you 

use?

12. As far as you are aware, is your division or company 

engaged with any industry consortia, standards 

organizations, or governing bodies specifically for advanced 

robotics and automation? If so, in which bodies do you 

participate and what are the underlying drivers for 

participation?

13. Generally, does your company conduct R&D related to the 

adoption, adaptation, and/or use of robotics and 

automation systems? If so, what are the broad objectives 

of that R&D (e.g., cost reduction, quality improvement, 

other)?
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: Advanced 

Robotics and Automation

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5

Industry Needs

Several industry-level needs have been identified, and 

discussions with experts have suggested measurement and test 

methods, material and process standards, reference databases, 

and general purpose technologies that could meet these needs.

Table B-1. General Industry-Level Needs for Robotics and Automation

Industry Needs

Infrastructure Technology to Help Meet 

Needs

Safe human-robot interactions

Universal standards for developers of robotic 

technologies and the application of these 

technologies in manufacturing settings with 

robots working in close proximity to people (see 

more below on sensing/perception for 

unstructured environments, relevant for intuitive 

HRI)

Test protocols, objective scientific and 

engineering data, reference databases, and 

other technical inputs into standards for safe 

HRI (power/force-limiting; speed/separation 

monitoring; hand-guided operation; safety-rated 

monitored stop)

Sensing and perception for unstructured 

(or less-structured) environments

Improved perception (and the ability to plan and 

re-plan its actions based on what it “sees” and 

“knows”) giving a robot greater autonomy, 

lessening its demand that its work environment 

meets stringent tolerances

Sensor registration & calibration

Performance characterization (benchmarks, 

testbeds, and technical inputs to standards to 

characterize the performance of systems, 

subsystems, and components)

Sensing/perception engines/architectures

Proof-of-concept robotics applications of KR&R 

(knowledge representation & reasoning)

Objective, low-cost performance 

characterization

Making it easier for robotics users to know what 

they are buying, easier for developers/suppliers 

to show what their systems do

Common performance metrics, objective data, 

testbeds, test methods, and benchmarks to 

characterize the performance attributes of 

advanced systems, subsystems, and 

components

Interoperability and modularity

Plug-and-play for system components, enabled 

by standards for physical and electronic 

interfaces, software interfaces/translators

Objective technical inputs into the standardssetting process: scientific and engineering data, 

benchmarks, testbeds, objective third-party 

testing of candidate technologies/configurations

Intuitive interfaces

Enabling rapid programming/training without 

specialized skills

Protocols to simplify the programming, training, 

and rapid re-tasking of robots

Standard programming language for industrial 

robotics analogous to SQL or HTML

Modeling and Simulation

Virtual factory floor allowing modeling and 

simulation, calibrated based on real-time data 

feed from robots, machine tools, sensors, 

control systems on the floor.

Robust, open, real-time operating system on the 

factory floor

Reference models, modeling frameworks to fully 

integrate robots into models of the 

manufacturing environment and enable robust 

simulation/prediction
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14. Do any of these needs apply to your company’s use of 

robotics/automation? In other words, if one or more of 

these needs were to be met, would it benefit your 

company? If so, how?

15. If several needs were discussed in question 14 as being 

relevant to your company, could you prioritize them? Which 

are the most important?

16. Are there needs not included in the table that you would 

include? If so, what are they?

17. If these needs were all met today, how would that impact 

your company’s R&D and production?

a. Changes in the types of robotics and automation 

systems used?

b. Changes in the range of products for which at least 

some manufacturing processes/tasks are automated?

c. Changes in the range of manufacturing 

processes/tasks that are automated?

d. Others?

18. Can you describe any types of impacts on your company if 

these needs were met, in terms of costs of production? A 

range is fine.

a. Changes in time needed to configure/test/validate 

product line or work cell?

(+ / − by roughly what %? __________%)

b.Changes in cost to configure/test/validate product line 

or work cell?

(+ / − by roughly what %? __________%)

c, Changes in unplanned downtime, when workers and 

equipment are idle?

(+ / − by roughly what %? __________%)

d. Changes in duration of product-development-to-

production cycle?

(+ / − by roughly what %? __________%)

e. Others?

(+ / − by roughly what %? __________%)
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19. To enable us to combine your responses with the responses 

of others and provide NIST with a sense of potential 

impacts at the industry level, could you quantify the 

impacts we have discussed in terms of the following 

metrics? A range is fine.

a. Cost of materials + / − __________%

b. Cost of energy/electricity + / − __________%

c. Cost of labor + / − __________%

d. Cost of capital equipment + / − __________%

e. Overall cost of production + / − __________%

20. Would you say that your answer to question 19 is 

representative of your industry (of companies in similar 

lines of business), or of only a subset? Please explain 

briefly how, if at all, the anticipated impacts for your 

company may be different from the industry as a whole, or 

how different industry segments may be affected 

differently.

21. Switching from thinking about costs to thinking about your 

company’s product offering, could you briefly describe what 

changes could be expected if these needs were all met 

today?

a. Changes in the quality of existing products?

b. Changes in the amount of customization within existing 

product lines?

c. Introduction of new products or product lines?

d. Others?

22. To enable us to combine your responses with the responses 

of others and provide NIST with a sense of potential 

impacts at the industry level, could you quantify these 

impacts in terms of a relative change in your company’s 

sales? A range is fine. + / − __________%

23. Would you say that your answer to question 22 is 

representative of your industry (of companies in similar 

lines of business), or of only a subset? Please explain 

briefly how, if at all, the anticipated impacts for your 

company may be different from the industry as a whole, or 

how different industry segments may be differently 

impacted.
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— Interview Guide for Robotics/Automation Systems Users

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24. Would you expect any changes in your company’s 

investment patterns or risk tolerance, if the types of 

technologies discussed above were made available? If so, 

what types of changes?

25. Are there any additional comments you would like to 

share?
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Appendix C:

Importance Scores
Appendix C — Importance Scores

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Table C-1. Rating of Importance of Capabilities/Needs (5=most important, 1=least 

important)

Safe Human-Robot InteractionsSensing and Perception for Unstructured (Or Less-Structured) EnvironmentsObjective, Low-Cost Performance CharacterizationInteroperability and ModularityIntuitive InterfacesModeling and Simulation

All Firms

4.2 4.0 2.7 3.6 3.6 3.3

Developers

Mean Score 4.3 4.0 2.5 3.6 3.0 3.1

End Users

Mean Score 4.1 4.1 3.0 3.7 4.2 3.6

Figure C-1. Rating of Importance of Capabilities/Needs (5=most important, 1=least 

important)
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Appendix D:

Vignette
Appendix D 

— Vignette

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Robotics Has the Potential to Transform Aerospace 

Product and Parts Manufacturing

Robotics and automation in aerospace manufacturing tend to be 

used in part fabrication and other tasks that occur early on in 

the final assembly process. A major aerospace OEM52 stated 

that its company uses robotics and automation in workcells that 

tend to be early in the assembly process, performing tasks such 

as manipulating composite panel covers, drilling, and fastening. 

Another OEM described a robot workcell with wiring and 

circuitry tasks and another workcell for precision welding. A 

supplier of aircraft fuselages said that his company used robots 

for painting and drilling.

In general, the adoption of robotics and automation technology 

in the industry has been limited, particularly to later stages of 

the assembly process, where OEMs are still employing many 

manual assembly techniques. When robots are used, they tend 

to operate in safety cages away from workers.

Looking to the future, the most important capabilities for the 

industry are safe human-robot interaction (HRI) and sensing 

and perception for unstructured, or at least semi-structured,

environments. Safe HRI is critical because of the labor-

intensive nature of later stages of the production process. To 

truly harness the power of robotics, they must be able to work 

alongside humans. Sensing and perception will not only support 

safe HRI, but it would also allow robots to deal with a highly 

variable production environment where a high variety of parts 

may be presented to the robot gripper in variable ways.

If the needed capabilities and associated enhanced technology 

infrastructure are met, this would likely crowd in investment by 

aerospace OEMs and large component suppliers that could use 

robots for new applications such as material handling, more 

dexterous work like riveting, and navigating semi-structured 

environments to collaborate safely with workers. Investment 

would be crowded in because the technologies would be less 

costly to integrate with existing production lines and would 

yield greater economic benefits compared with current robotics 

technology.

 

52 OEM, or original equipment manufacturer, is used to describe the 

manufacturers that assemble and sell the final aircraft.
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Industry experts provided context on the particular benefits 

that enhanced infrastructure technology would have.

Benefit #1: Freeing up Skilled Labor and Improving 

Quality

Highly skilled technical workers in the aerospace industry are 

also commonly responsible for later-stage, manual assembly 

tasks. With enhanced infrastructure technology, it would be 

possible to safely slot in collaborative robots next to humans. 

Deploying collaborative robots would free up highly skilled 

aerospace workers for other less tedious tasks. It could also 

improve quality, which tends to erode when humans do too 

much ergonomically taxing, monotonous, or dull work.53 One 

industry expert thought that riveting quality would improve 

with robots because they are more consistent and precise than 

humans.

Benefit #2: Adding Predictability to the Production Cycle

Increased adoption of robotics technology in the industry could 

add more predictability to the production cycle. One aerospace 

manufacturer pointed out that as more manual labor is 

removed from the production process, one direct result would 

be less variability in production cycles because of less human 

error. One OEM described the unpredictable nature of 

production in terms of non-recurring labor costs that are borne 

out of rework and last-minute changes when inspections show 

that a component may have been not installed properly or at

all.

Increased predictability is valuable for any manufacturer 

because it allows them to plan and coordinate supply chain 

activities more effectively, such as purchasing inputs and 

delivering to customers on schedule.

Benefit #3: Reducing Work-in-Process Inventory and 

Reducing Lead Times

Aerospace assembly production cycles last for months and even 

years. As a side effect of the long production cycles, a 

tremendous amount of capital is embodied in work-in-process 

inventories that accumulate as a plane is being built. (These 

inventories represent a substantial opportunity cost for 

 

53 This is true across many industries, not just in aerospace 

manufacturing and not just in the manufacturing sector.
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aerospace manufacturers because it is capital that they cannot

invest in other purposes.)

Advanced robotics, which has the potential to speed up 

throughput and production cycles, could lead to a reduction in 

work-in-process inventory investments. It will take time for 

manufacturers to take full advantage of advanced robotics and 

enhanced technology infrastructure because in some situations, 

once workcells and production lines are set up, they build the 

same product for 20 to 30 years. If the impacts from advanced 

robotics are large enough, that may provide enough of an 

economic incentive to retrofit or re-plan workcells. Improved 

production efficiency would add value for customers by 

reducing lead times for new orders.

Importance of Other Advanced Manufacturing 

Technologies

Robotics and automation will have a greater effect when they 

are combined with other advanced manufacturing technologies 

such as smart manufacturing and additive manufacturing. As 

one aerospace manufacturer stated, “Robotics is one aspect of 

a number of things that have the ability to improve the 

efficiency. Robots and automation alone will not make a 

massive impact on our business in terms of where they are 

today.”