A new generation of robots can handle unstructured tasks
By: Clinton J. Smith
Tags: Robots, Automation, Industry 4 0, Robotics, Manufacturing

Automotive factory with manufacturing automation

Controlling task variance has been critical to the last century of (traditional) automation

Task Variance Explained: How replicable is a given task? Do the objects being handled vary in any way (e.g., different dimensions, colors, etc.)? Does the orientation or pose of the objects handled vary? Automobile manufacturing is a classic example of an industry that has tightly controlled task variance, thereby enabling a significant degree of traditional automation. On assembly lines that produce millions of identical objects, robotic systems are programmed step-by-step to perform predefined tasks (e.g., weld joint here, drive rivet there, over and over again).

Probability density functions with different standard deviations

One can think about task variance from a statistical perspective: A task that has a high degree of variability would result in a “wider” statistical distribution, meaning that, for example, different types of actions are more likely to be required to do a particular job. This can be graphically represented by the “a=1/1” probability density function in the above image. Virtually all existing industrial robot installations are successful because the “task variance” is tightly controlled so that a pre-programmed, identical action, can reliably work (e.g., “a=1/10”). Actions are controlled with sub-millimeter precision and millisecond-level timing. But only a limited number of companies have the scale and capital to pursue this type of approach. Millions of identical units must be run in order to justify non-recurring engineering (NRE) costs.

Manufacturing for the rest of the world

Yet the rest of the world of manufacturing has task variance that falls outside such a narrow distribution window (e.g., a=1/3 ). Varying product lines, batch orders of customized products, food handling, and more mean that millions of identical, pre-programmed actions are simply not possible, making the NRE cost associated with traditional automation far beyond any return on investment for a given task.

Due to necessity, the rest of the manufacturing world utilizes human labor — the U.S. alone devotes nearly $1T/year to “physically predictable tasks”. We, at RIOS, have interacted with hundreds of customers (ranging from multinationals to mom-and-pop shops) and toured dozens of factories.

Picking and placing items into a fixture

We hear the same themes over and over again:

Labor is scarce — we rely on temp labor We can’t retain labor Labor quality is uneven and unreliable

Why is this? Well, let’s run a thought exercise. Grab a set of common household items — maybe a bunch of pens or as many 6-packs as you can hold. Place them on one kitchen counter, grab them, and place them on another kitchen counter — maybe 2–4 feet away. No big deal right? Now try doing that at a rate of placement (i.e., a round trip) every 2 seconds for 8 hours straight. This particular thought experiment is a simplistic example of an e-commerce pick and place use-case. Amazon is famous for holding more complicated competitions of this sort. To give an idea of the difficulty of this problem to automate, even with its “infinite” resources, Amazon still relies on humans for their pick and place.

Variants of this kind of task are embedded all throughout the worldwide economy. In fact, our discussions with customers have yielded several broad categories of tasks that cannot be automated with traditional methods:

Static & dynamic assembly of batch runs or changing SKU’s

Assembling 2 or more objects either in a static staging ground or on a moving conveyor belt

Examples include sliding one part onto or over another, placing a component into a socket or fitting

Pick and place of varying objects

Sort through jumbled bins to place into packaging or onto a conveyor or staging system

Bin to box, conveyor to box, box to the conveyor, removing defective items from a line, sorting items on a conveyor

Kitting (i.e., bringing together diverse parts from different locations to a common container)

Machine tending

Load/unload parts into or out of a machine

Interact with machines to perform some operations

RIOS solves manufacturing automation for the rest of the world

RIOS has built the world’s most advanced and dexterous robots by leveraging the confluence and increasing maturity of a number of technological trends: Integrated circuits (ICs) designed for the internet of things (IoT), deep learning, advanced circuit manufacturing techniques used to make smartphones, open-source efforts in robotics, operating systems, networking, security, the manufacturing/maker revolution, etc.

RIOS technology uses “brain-hand-eye” coordination to manipulate objects as humans do

By leveraging these trends — and creating patented best-in-the-world haptic technology — we build robotic systems that mimic the “brain-hand-eye” coordination of humans. RIOS technology enables us to tackle the “last mile” of automation. When our robots assemble parts, they recognize the parts and the parts’ relationship with the world and the robot itself, they feel the vibrations resulting from various points of friction as the parts are assembled together. When grabbing soft or delicate objects (e.g., food, produce), our robots are able to use optimal grasp so as not to bruise the products. Also, as a FANUC integrator, we use FAST robots. In fact, we are able to handle products as fast as the laws of physics will allow because we can measure and detect slip in real-time — a capability no other company has conquered.

Why are all these capabilities so important? Much of the manufacturing world operates in the 40–100 items/minute regime (depending on object mass, size, etc.), the limit at which humans are dependable at repetitive tasks over sustained periods — even if it burns people out. Without these capabilities that RIOS has developed, it’s extremely difficult to automate to meet that mark in a sustained way — unless of course, a business can afford the significant capital expenses of tightly controlling the task variance.