NISTIR 7901

Best Practices and Performance 

Metrics Using Force Control for 

Robotic Assembly

Jeremy Marvel

Joe Falco 

http://dx.doi.org/10.6028/NIST.IR.7901
NISTIR 7901

Best Practices and Performance 

Metrics Using Force Control for

Robotic Assembly

Jeremy Marvel

Joe Falco

Intelligent Systems Division

Engineering Laboratory

http://dx.doi.org/10.6028/NIST.IR.7901

November 2012

U.S. Department of Commerce 

Rebecca Blank, Acting Secretary

National Institute of Standards and Technology 

Patrick D. Gallagher, Under Secretary of Commerce for Standards and Technology and Director
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Best Practices and Performance Metrics Using Force Control for Robotic Assembly

Jeremy Marvel, Joe Falco

Table of Contents

DISCLAIMER .................................................................................................................................... 2

I. Introduction.......................................................................................................................... 3

II. Robotics in Assembly .......................................................................................................... 3

III. Assembly and Robot Dexterity............................................................................................ 4

IV. Assembly Strategies............................................................................................................. 5

V. Force Control ....................................................................................................................... 5

A. Explicit Force Control.......................................................................................................... 6

B. Stiffness Control .................................................................................................................. 6

C. Impedance Control............................................................................................................... 6

D. Admittance / Accommodation Control................................................................................ 7

E. Implicit Force Control ......................................................................................................... 7

F. Natural Admittance Control................................................................................................. 7

VI. Commercial Robot Solutions............................................................................................... 7

VII. Metrics for Assessing Force Control ................................................................................... 8

A. Force Control Metrics.......................................................................................................... 8

B. Force-Based Assembly Metrics........................................................................................... 9

VIII. Assessing the Performance of Robotic Assembly ............................................................. 10

IX. References.......................................................................................................................... 13
2

DISCLAIMER

Certain trade names and company products are mentioned in the text or identified in certain 

illustrations. In no case does such an identification imply recommendation or endorsement by the 

National Institute of Standards and Technology, nor does it imply that the products are 

necessarily the best available for the purpose.
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I. INTRODUCTION

Historically, mechanical assembly was predicted to be the dominant application domain of 

industrial robots; yet assembly tasks are still primarily addressed by manual labor. New 

advances in force control (FC), machine vision, and robot dexterity are enabling industrial 

robots, such as four to seven degree of freedom (DOF) arms and selective compliant 

assembly robot arm (SCARA) manipulators, to catch up with the original visions of robotic 

assembly. As these technologies progress, performance metrics and associated test 

methodologies are needed to unify research efforts, characterize the state of technology, and 

to provide a means for end-users to evaluate the capabilities of robotic assembly systems.

This report describes the current state of force-controlled mechanical assembly, and 

highlights key robot technologies and algorithms that enable this utilization. Section II gives 

an overview of the use of robots for assembly tasks. Section III provides insight into the 

role of dexterity in assembly robotics. Section IV discusses various strategies used by 

automated systems to enable assembly. Section V describes frequently-used robot force 

control algorithms. Section VI provides an overview of force control options currently 

commercially available from robot manufacturers. Section VII discusses the metrics used to 

assess both force control stability and assembly efficacy. Section VIII introduces some 

concepts for performance testing utilizing the defined metrics. 

II. ROBOTICS IN ASSEMBLY

Within the domain of industrial automation, the roles of robots have been relegated largely 

to minimal-contact, repetitive, position-controlled tasks such as spot welding, spray painting, 

and the transportation, packaging, and handling of materials. Such applications use the 

known physical properties and feedback properties of the robot to localize the robot as it 

moves through the Cartesian space. Robots commanded by position control algorithms can 

be programmed to perform their tasks independently with little knowledge of the 

environment in which they operate. However, strict position-controlled robots are incapable 

of performing high tolerance assemblies due to the cumulative uncertainties of assembly 

parts, links, axles, fixtures, and end effectors that exceed component clearances. Unable to 

overcome these shortcomings, position-controlled robots achieve assemblies through the use 

of chamfers, external feedback mechanisms (e.g., ultrasound or light range), and position 

compensation tools such as remote center of compliance (RCC) devices that passively 

accommodate forces and torques to allow the parts being held to adjust to minor lateral and 

angular uncertainties. Although these tools allow for the assembly of components, they are 

not general-purpose solutions, and the product features which allow them to compensate for 

cumulative uncertainties are not conducive to current trends in shrinking component designs.

Compliant motion control is a robot control method that modulates robot position and 

velocity based on measured contact forces [1]. Using this sense of touch, robots can perform 

tasks requiring physical contact between the end effector or workpiece and the 

manufacturing process. Despite these advances, however, due to bandwidth limitations and 

unreliable system performance [2] industry has been a slow adopter, and processes requiring 

extended contact between the manipulator and assembly components have largely remained 

the task domain of manual labor forces.
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Though force control has been an active field of research for decades, the industrial world 

has been slow to adopt force-based practices in automating assembly tasks [3]. There are 

several factors that have influenced this slow migration, not the least of which is the cost 

involved, both in terms of the time necessary to implement and the steep learning curve. 

Some of the most prohibitive aspects are the failed promises of reliability and throughput; 

reliable solutions are not fast, and fast solutions are not reliable. While a robot can be 

programmed to be an expert at a single assembly problem, generalizing this expertise across 

all assemblies is difficult. Some of these problems are being addressed with new concepts 

of collaborative robotics where a human compensates for a robot’s inadequacies to complete 

an assembly operation. The safety implications of robot operation within such a close 

proximity to a human presents additional control requirements [4, 5].

Emerging research topics in task-driven human-robot collaboration have focused on the 

direct interaction between a human operator and an industrial manipulator. Robots intended 

to work with humans often incorporate force-sensing technologies and control algorithms to 

enable human-guided training [6], object handling [7, 8], and safe collision handling [9].

III. ASSEMBLY AND ROBOT DEXTERITY

A limiting factor in the implementation of robot assembly cells is the dexterity of the 

manipulator and end effector. Though the utility of a standard 6DOF robot arm with a 

traditional gripper has been demonstrated through decades of implementation, the actual 

number of tasks capable of being performed by such configurations is limited. Assembly 

configurations must be designed with the limitations of the robot in mind. Such restricting 

factors include the robot’s reach, lift capacity, sensing capabilities, and dexterity.

It is generally agreed that increasing the dexterous capabilities of the robot or the gripper 

will enable the robot to overcome task obstacles by being more flexible [10]. However, 

there is relatively little consensus regarding the form and measurable degree of such 

dexterity [10, 11]. For example, increasing system dexterity may be accomplished by either 

adding additional linkages in the arm kinematics or by attaching a gripper featuring withinhand dexterity (such as anthropomorphic or multi-fingered hands with numerous 

independently-controllable degrees of freedom). Both extend the repertoire of abilities of 

the robot, but each in its own distinct way and each adding complexity in the control 

domain. As an example, robots with redundant kinematics are more nimble and capable of 

navigating obstruction-laden work zones, whereas dexterous grippers enable reconfigurable 

grasps and flexible manipulation of work objects.

Gripper dexterity, in particular, has been the topic of considerable research within the past 

few years. Though most investigations have focused on grasping (e.g., algorithms, 

modeling, performance, and optimization [12-16]), investigations into within-hand 

manipulation of parts have the potential for positively impacting a robot’s capabilities for 

assembly. Capabilities such as regrasping (releasing and reacquiring objects to change their 

position and orientation), finger gaiting (alternating contact between fingers in order to 

stabilize or regrasp parts, e.g., [17]), finger pivoting (where two fingers grasping an object 

form an axis of rotation, and a third finger rotates the object along that axis, e.g., [18]),

stable in-grasp manipulation of parts for repositioning and reorienting [19], and unstable ingrasp manipulation [20, 21].
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IV. ASSEMBLY STRATEGIES

Many common implementations of force control for assembly implement force-driven 

motion primitives such as push, pull, and twist to join two or more parts together. Such 

approaches require controlled fixturing to ensure some level of position and process 

certainty. Further, they are reminiscent of the classic position-controlled approaches of 

robotics in that the manipulator is simply performing a set of basic motions on parts with 

little in-line process validation, and they do not reliably handle part variance.

To address the shortcomings of the motion primitive approach, adaptive techniques in the 

form of event-based search strategies recognize and attempt to compensate for part and 

position variance. In many cases a given assembly task may have numerous strategies 

defined for its completion. For instance, a peg-in-hole assembly may be completed by 

“dumb” searches in which the peg to be inserted is moved around a candidate hole position 

(either randomly, or by a structured geometric pattern like rasters [12] or spirals [23]) until it 

can be pushed in. Alternatively, more explicit methods can be employed that intelligently 

probe the candidate hole position and, based on the effector position and force moments, 

accurately identify the location and orientation of the hole into which the peg will be 

inserted [24]. Other assemblies that are more specific in nature (for example, automobile 

clutch assemblies) can be parameterized and generalized [22], or broken down into separate 

motion primitives [23].

Further, most mechanical assemblies employ chamfers to eliminate sharp edges that lead to 

stress concentration. These may also be used to provide tactile clues for parts guidance and 

permit the use of passive tools like RCCs [25], though it has been demonstrated that 

insertion assemblies can be performed in their absence [26]. Other efforts to address the 

assembly problem have focused on modeling tasks for assessment and optimization. Such 

models are typically abstract in nature, and are used to describe solutions for design and 

scheduling problems through relational representations [27, 28], component connections

[29], assembly “intent” [30], and cumulative entropies [31].

V. FORCE CONTROL

Tasks that require a robotic manipulator to directly interact with its environment are 

dependent on the robot’s ability to sense the world it touches. Merely sensing forces and 

torques, however, is insufficient for understanding them. These sensed forces must be put 

into context of the robot’s current configuration, and must subsequently result in a marked 

and appropriate change in behavior. Compliant motion control (or force control, or torque 

control) is a reactive control model in which a tight control loop is expected to make 

comparatively simple decisions based on the measured signal outputs of a variety of sensors 

measuring velocity, position, acceleration, and force. These four variables comprise the 

basic components of a myriad of force control methods. 

The application of force control is essentially one of relating measured forces to one or more 

system variables in the form of a mass-spring damper for tuning and stability. There exists a 

wide variety of compliant motion control algorithms relating measured force to virtually any 

combination of position, velocity, acceleration, and applied force [32-34]. The remainder of 

this section consists of a discussion of the principal force control algorithm types: explicit 

force control, stiffness control, impedance control, admittance (or accommodation) control, 

implicit force control, and natural admittance control.
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A. Explicit Force Control

The most basic active force control algorithm is that of explicit force control, in which 

the measured forces are related to the desired, applied forces [35]. The sensed forces, FS, 

are used in a tight feedback loop to generate force errors, which are then used directly to 

adjust the commanded force variables, FC. In schematic form, this can be illustrated as is 

shown in Figure 1.

Figure 1: Generalized explicit force control. The differences between the 

measured and commanded force values drive the robot’s motions.

The force control law is typically simple in implementation, such as the proportionalintegral (PI) control illustrated in Figure 2, where the error between the measured and 

commanded/sensed forces, e, results in more or less force being applied over time, t. The 

rate of convergence is controlled by the P and I gain values, KP and KI respectively. 

Figure 2: Explicit force control with PI control. Proportional and integral 

control values, P and I, drive the convergence between measured and 

commanded force values, FS and FC.

B. Stiffness Control

One of the most basic and fundamental variants of force control for robotics, stiffness 

control, softens the impacts and incurred forces of the robot’s end effector by mimicking 

the properties of springs. The trivial implementation of this is to literally utilize 

mechanical constructs composed of springs and dampers inline with the robot effector. 

So-called passive stiffness control mechanisms are well-established and understood, with 

tools being used to compensate for minor errors in orientation for insertion tasks [36]. In 

contrast, active stiffness control effectively turns the robot into a programmable spring, 

with the stiffness of the robot controlled by means of force feedback in the closed-loop 

system. Stiffness control is most appropriate for low-bandwidth applications that focus on 

accuracy in force measurements (e.g., [37]).

C. Impedance Control

Impedance control measures the user’s motion input, and then feeds back a reaction force 

to the operator [38]. Essentially, the output force is driven by the operator’s commanded 

velocity. The relationship between the input velocity and the applied force is classically 

referred to as the mechanical impedance of the robot. Impedance control attempts to 

simplify the control paradigm by maintaining the dynamic relationships between robot 

variables such as tool center point (TCP) position and applied force, rather than

maintaining the individual variables themselves (as is typical with most force-based robot 

control algorithms). Providing high bandwidth force responses for stable tool contact 

with a rigid object, impedance control is typically used for high-speed applications such 

as polishing, metal removal, and certain assembly tasks.
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D. Admittance / Accommodation Control

The opposite of impedance control, admittance (or accommodation) control [25]

measures the forces exerted by the user, and then feeds them back in the form of velocity. 

In short, the velocity of the robot is a function of the commanded force. The stiffness and 

dampening properties of the system can be adjusted to increase or decrease the velocity 

of the robot end effector based on the applied/commanded forces. 

E. Implicit Force Control

In contrast with the explicit force control loop discussed earlier, there is no actual force 

feedback into the system with implicit force control. Instead, the forces exerted by the 

robot’s end effector are implied based on a priori knowledge of applied torques given 

measured joint currents. In one such implementation, a variation of passive stiffness 

control, a robot outfitted with elastic joints has its TCP moved to pre-defined positions on 

the surface of an object (e.g., [39]). The resulting applied forces at those positions are 

inferred based on a priori knowledge obtained from earlier measurements. Such methods 

however, are impractical given part variations and encoder drift. A more common 

instantiation, however, infers applied forces based on either applied torques or measured 

currents at each joint of the manipulator (e.g., [40]). In both cases, the applied forces are 

measured and calibrated using an external force transducer.

F. Natural Admittance Control

Another algorithm designed to imitate the properties of mechanical systems, natural 

admittance control implements virtual springs and dampers to control the robot’s 

dynamics as it is drawn toward a controllable virtual attractor [41]. The attractor 

interacts with a reference point, fixed relative to the robot’s tool frame; virtual 

impedances and physical exerted forces interact to effect motion. The robot equilibrates 

virtual and physical forces by accelerating in the direction of the net force, scaled by the 

inverse of the robot’s inertia, producing both stable and responsive interactions with the 

robot effector.

VI. COMMERCIAL ROBOT SOLUTIONS

This section summarizes the commercially available robot force control and assembly 

options available at the time this report was written. Table 1 provides a brief summary of 

the robot manufacturers and their force control solutions based on a review of available

product manuals, brochures, and specifications. 

The information provided in Table 1 is intended to establish a brief overview of the forcebased control options available from various industrial robot manufacturers. For each 

manufacturer we indicate whether the control algorithms and force/torque sensors are 

provided either as a function that comes standard with the purchase of a robot or as an 

elective purchase option. Similarly, we specify which force control methods are provided 

by the manufacturer, as well as which motion primitives are enabled while force control is 

active. Though all of the manufacturers in Table 1 offer some form of force control, 

relatively few offer a force-based assembly option. Those that do have an assembly option 

are indicated, as well as whether or not they come standard with the force control option.
8

Table 1: Overview of Robot Manufacturers Who Offer Force-Based Control Options

Adept ABB Barrett DENSO EPSON

FC Purchase 

Option (Control)

Standard (guard/ 

protection 

function)

Option Standard Standard

Option (guard/ 

protection 

function)

FC Purchase 

Option (Sensor) Option 3rd party option Standard/option None

Standard (joint 

torques)/option 

(force)

Native Force 

Control Method None Accommodation 

control

Implicit force 

control

Per-axis implicit 

force control None

FC Assembly 

Option None Native with FC 

option None None None

Force-Based 

Motion 

Primitives

None Constant push, 

search primitives Cartesian motions None None

FC-Enabled 

Robot Models

4/6DOF arms, 

SCARA

6DOF arms, 

14DOF prototype 4/7DOF arms 4/6DOF arms 4/6DOF arms, SCARA

FC Sensor 

Location TCP TCP Joints, TCP Joints TCP

FANUC KUKA MotomanYaskawa RRC Stäubli

FC Purchase 

Option (Control) Option Standard (7DOF 

arm)/option Option Standard Option

FC Purchase 

Option (Sensor) Option

Standard (7DOF 

arm)/ 3rd party 

option

3rd party option Standard 3rd party option

Native Force 

Control Method

Impedance 

control

Explicit force 

control, 

impedance 

control (7DOF 

arm)

Explicit force 

control

Impedance 

control, stiffness

control

Accommodation 

control

FC Assembly 

Option

Native with FC 

option

None (option in 

pipeline) None None None

Force-Based 

Motion 

Primitives

Constant push, 

search & insert 

primitives

Constant push, 

force-based linear 

and joint motions

Constant push

Force-based 

linear and joint 

motions

Force-based 

linear and joint 

motions

FC-Enabled 

Robot Models

6DOF arms 

(limited models) 6/7DOF arms 6/7DOF arms 7DOF arms 6DOF arms, SCARA

FC Sensor 

Location TCP Joints (7DOF 

arm), TCP TCP Joints TCP

VII. METRICS FOR ASSESSING FORCE CONTROL

A notable shortcoming of the assessment of force control efficacy is the fundamental lack of 

comprehensive (and widely-accepted) metrics for evaluation. Such evaluations are difficult 

to quantify as they are generally specific to the respective application, and may further be 

arbitrary according to the user’s preferences. Within the domain of force control, however, 

certain qualifiable trends in assessment do repeat. For all such assessments, it is presumed 

that performance evaluations are measured by competent users utilizing traceable procedures 

and equipment as defined by the applicable metrological fields.

A. Force Control Metrics

As was discussed earlier, force control algorithms are based on common feedback loops 

with tunable dampers, springs, and associated gains. The algorithms provide a means of 

physically interacting with the world while limiting the potential for damaging either the 

objects within it or to the robot itself. This goal cannot be achieved if the robot’s actions 

are not tightly controlled. As such, one of the largest overlying themes of robot 

performance evaluation is that of stability. A properly damped system, when 
9

encountering variances in incidental force measurements, will react to said forces and 

eventually settle to a stable (sustainable) configuration until new changes in force 

measurements are encountered. An improperly damped system, however, will resonate, 

with a worst-case scenario resulting in the magnitudes of the oscillations increasing until 

a catastrophic event occurs.

Settle Stability: How long does the system take to settle? A common test of this is for 

the robot tool tip to move toward a stable surface until initial impact, and then record the 

length of time necessary for the robot to settle such that it maintains a consistent contact 

force with a surface, a limit cycle with a constant frequency and magnitude. A stable 

system will settle fairly quickly—typically after only one or two consecutive “hops” 

(brief moments of motion reversal along an approach vector) of decreasing magnitude—

whereas an unstable system will hop indefinitely with varying (or, worse, increasing) 

magnitudes.

Obstruction Stability: If an immovable object keeps the robot tool from reaching a goal 

state, does the robot maintain stable, constant contact, or does the incurred force 

increase? A stable system will impact, hop slightly due to motion reversal, and then 

come to rest against the object quickly. If there is some perceptible slope to the incident 

force, the robot may move along the lowest-resistant route. This permits the robot 

effector to trace up a slope or around an object in an effort to complete its program.

Control Switch Stability: More of a metric for assessing the capabilities of the robot 

controller, determining the stability of a robot when switching between control structures 

(such transitioning from position control to impedance control) can prove indicative of 

the system’s handling of force and position errors. A stable system will transition from 

one control structure to another smoothly and without incident, while an unstable system 

may jerk when transitioning due to unequilibrated offset and encoder errors or lapses in 

time response for poorly-tuned control loops.

Surface Cohesion (Force Profiles): While moving along non-planar surfaces, a robot is 

often required to maintain constant surface contact for a given task such as paint removal, 

welding, and deburring. For some applications, the force with which it maintains contact 

is as important as the maintenance of contact. Stable systems will respond quickly to 

changes in the surface and maintain a relatively constant applied force, while less 

responsive systems may skip or gouge the surface. Similarly, the speed at which the 

robot moves along a surface and maintains force stability is also a factor in assessment

for relevant applications.

Incurred Force Limitations: As the robot performs motion tasks, it is likely to incur a 

number of incident forces while interacting with objects. For safety and quality 

assurance purposes, it is important that these incurred forces do not exceed defined limits. 

Safe responses to these forces include reacting to the forces along a vector in the 

direction of the applied force, slowing/braking the robot, or halting the robot altogether.

B. Force-Based Assembly Metrics

Numerous automation tasks and process controls can be addressed by means of relatively 

simple damped system solutions such as proportional–integral–derivative (PID) or massspring dampers. However, complex tasks such as multi-staged assemblies with 

component location uncertainties require an adaptive system capable of automatically 
10

adjusting to both identify and compensate for changes in operational conditions. This 

capacity may either be explicitly programmed or automatically adapted to via machine 

learning. In either case, some form of feedback basis is required for the optimization 

process; this task-specific feedback (e.g., time or bandwidth) is considered for process 

optimization, and is distinct from the feedback for force-control parameter tuning. In this 

section, we briefly describe a number of metrics used for assembly optimization.

Assembly Time: Efforts in automating mechanical assemblies is for naught if the 

resulting product cannot be completed quickly. As such, a common metric for 

automation efficacy is the required time to complete an assembly task [42, 43].

Success Rate: As important as the time required to complete an assembly, successfully 

completing assemblies rather than prematurely aborting them due to time constraints or 

improperly seated parts is an integral component of process automation. For this reason, 

the number of successful assemblies [44] is often seen as a useful metric for system 

throughput and stability.

Incurred (Maximum/Average) Force: An assembly task requires part-on-part contact, 

and as such runs the risk of parts damage. A trivial solution to an assembly attempt is to 

merely apply more force, which, while potentially completing the assembly faster, also 

risks the structural integrity of the parts, the robot, the tooling, and the force sensors, 

themselves. As such, one would prefer to minimize the forces either incurred or 

encountered to minimize damage potential [42]. Unfortunately, there is no “ideal” value 

for an incurred assembly force. Instead, the material properties of the components being 

assembled will often dictate the softness of touch for handling. Small and fragile parts 

require gentle touch, whereas more sturdy materials can withstand more punishment. 

Ideally, assembly forces are controlled to be minimally sufficient. A notable subset of 

the incurred force metric is the measurement of impact forces. Often three to five times 

larger than the forces incurred throughout the process of completing an assembly, force 

spikes resulting from the initial impact between the robot tool and parts being assembled 

are used as indicators of the robot’s ability to sense and quickly react to forces [45, 46]. 

Well-tuned algorithms and systems react quickly, mitigating the impact force.

VIII.ASSESSING THE PERFORMANCE OF ROBOTIC ASSEMBLY

This section presents the concept of an independent measurement system developed by the 

National Institute of Standards and Technology for assessing the performance of a robotic

assembly against the proposed metrics found in section VII. Figure 3 shows a prototype 

design for an independent measurement system. The design incorporates a 6-axis load cell 

fixtured between two base plates. The bottom base plate supports the overall system while 

the top base plate is designed to accept a set of modular artifacts. Each artifact is designed 

to address one or more control or assembly metrics.

While it is possible to access force data directly from a robotic system’s force sensing 

system and derive the defined metrics, these measurements would be based on the inherent 

properties of the system under test. An independent measurement system allows for 

comparative metrics between systems without effects such as force accuracies, data 

latencies, and the effects of tooling offsets. 
11

Figure 3: Independent measurement system for evaluation of force 

control and force-based assembly metrics

Figure 4 shows an artifact for measuring obstruction stability on a force controlled robotic 

system capable of adapting tool paths in the presence of an unanticipated slope. The artifact 

contains several degrees of slope from slight to aggressive at various artifact locations to 

support several tool path directions and eliminate the need to reorient the artifact relative to 

the robot coordinate frame. 

Figure 4: Test artifact for obstruction stability with provisions for 

obstruction avoidance 

Figure 5 contains an artifact for measuring the settle stability and obstruction avoidance 

capabilities of a system and another artifact for measuring the control switch stability of a 

force based robotic assembly system. Settling stability is measured as the robot tooling 

collides with each surface of the artifact when robot tool paths are programmed to move 

toward the load cell center along the primary axes and at 45° XY planar trajectories. In the 

case of control switch stability, the robot tooling is inserted into the hole designed to allow 

slip but not any XY movement until the tool just touches the hole bottom in the Z direction. 

A control switch is performed and the reactive forces are measured.
12

Figure 5: Test artifact for settle stability and obstruction avoidance (left)

and control switch stability given a peg inserted into a hole (right)

Figure 6 contains a proposed artifact for measuring the forces associated with a hole 

searching algorithm and another artifact for measuring the forces associated with the 

insertion of a sun gear into a planetary gear system. These component assembly operations 

are designed such that the center of the assembled component resides at the center of the 

independent load cell.

Figure 6: Test artifact for force based assembly metrics for 

hole searching (left) and planetary gear insertion (right)

The artifacts presented above represent a subset of artifacts that will be developed for this 

independent measurement system for evaluating force-based robotic assembly. This 

measurement system along with the defined performance metrics will help robot suppliers 

better specify robot capabilities. The system will also provide a mechanism for the research 

community when developing new force-based control algorithms for assembly. Finally, the 

measurement system will provide end users with a mechanism for assessing robotic 

assembly system capabilities prior to limited production testing. As these measurement 

system concepts progress, multiple component assembly operations may be designed into

assembly kits. An assembly kit is a collection of components classed by specific

manufacturing sectors that can be used to assess the overall performance of a robotic 

assembly system. In addition, test methods will be derived that will include measures for 

assessing dexterity in conjunction with assembly processes.
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