Thinking concerning the future of robots and autonomy is exciting; driverless cars, lights-out factories, urban air mobility, robotic surgeons available around the globe. We’ve seen the building blocks get together in warehouses, retail stores, farms, and on the roads. It is currently time to build robots for humans, not to replace them.
We still have a considerable ways to go. Why? Because building robots that intend to work fully autonomously in a physical world is hard.
Humans are incredibly good at adapting to dynamic situations to achieve an objective. Robotic and autonomous systems are incredibly powerful at highly precise, responsive, multivariate operations. A fresh generation of companies is turning their attention to bringing the 2 together, building robots to work for humans, maybe not replace them, and reinventing several industries in the process.
Innovation through limitation
New ways of ML, such as for instance reinforcement learning and adversarial networks, have transformed the speed and capability of robot systems.
These techniques work well when:
- Designed for well-known tasks.
- Within constrained environments and limited variable change.
- Where most end states are known.
Where the chances of unforeseen situations and ‘rules’ are low, robots could work miraculously a lot better than any human can.
An Amazon robot-powered warehouse is an excellent illustration of well-characterized tasks (goods movement), in constrained surroundings (warehouse), with limited diversity (structured paths), and all end states are known (limited task variability).
Robots in a complex world
What about in a less structured environment, where you can find greater complexity and variability? The possibility of errors and unforeseen situations is proportional to the complexity of the process.
In the physical world, what is a robot to do when it encounters a scenario it never seen before? That question conflicts with the robots’ understanding of the expected environment and has not known end states.
The conflicted robot is exactly the challenge organizations are facing when introducing robots to the physical world.
Audi claimed they’d hit level 3 autonomy by 2019 (update: they recently gave up). Waymo has driven 20 million miles yet operationally and geographically constrained.
Tesla reverted from the fully robotic factory approach back to a human-machine mix, the organization stating, “Automation simply can’t deal with the complexity, inconsistencies, variation and ‘things gone wrong’ that humans can.”
Yes — this complex issue will soon be figured out — but the situation is not solved yet.
To solve these dilemmas in the physical world, we’ve implemented humans as technology guardrails.
Applications such as driverless cars, last-mile delivery robots, warehouse robots, robots making pizza, cleaning floors, and much more, can operate in real life thanks to ‘humans in the loop’ monitoring their operations.
Humans are acting as either remote operators, AI data trainers, and exception managers.
The ‘human in the loop’ has accelerated the pace of technology and opened up capabilities we didn’t think we’d see within our lifetime, since the examples mentioned earlier.
At the same time frame, it has bounded the employment cases to which we build. When we design robotic systems around commodity skill sets, the range of tasks is bound to those just those skills.
Training and operating a driverless car, delivery robot, or warehouse robot all require the exact same generally held skill sets.
As a result, what robots can handle today primarily cluster round the ability to navigate and identify people/objects.
As these companies bring their solutions to market, they quickly realize two realities:
(1) Commodity tasks allow it to be easier for others to also attempt a similar solution (as seen with the amount of AV and warehouse robot companies emerging over the past few years).
(2) High labor liquidity depresses wages, thus requiring these solutions to fully replace the human, maybe not augment, in high volumes to generate any meaningful economics. E.g., Waymo/Uber/Zoox needs to remove the driver and operate at high volumes to turn a profit sooner or later.
The result of the commodity approach to robotics has forced these technology developers to completely replace the human from the loop to become viable businesses.
Changing the intersection of robotics and humans
The open question is: is this the proper intersection between machine and human? Is this the most effective we can do to leverage the precision of a robot with the creativity of a human?
To accelerate what robots are capable of doing, we need to shift focus from trying to replace humans, to building solutions that put the robot and human hand-in-hand. For robots to find their way in to critical workflows of our industries, we needed them to augment experts and trained technicians.
Industries such as general aviation, construction, manufacturing, retail, farming, and healthcare could possibly be made safer, more efficient, and much more profitable. Changing the human’s role of operator and technician to manager and strategist.
Helicopter pilots could free themselves from the fatiguing balance of flight and control management. Construction machine operators could focus on strategies and exceptions rather than repetitive motions.
Manufacturing facilities could take back workers to focus on throughput, workflow, and quality, as opposed to tiring manual labor. Retail operators could focus on customer experiences as opposed to trying to keep up with stocking inventory.
These industries all suffer with limited labor pools, highly variable surroundings, with little technology, and high cost of errors. Pairing robotic or autonomous systems that work together with experts could invert from the set of dynamics compared to commodity use cases.
Companies could build solutions that need only to augment the operator, not replace him or her, to meaningfully change the economics of the operation.
Building for an expert-robot generation
The current generation of technology innovation is starting, with a new generation of organizations using robotics and autonomy to change the operating experience across industries.
- Innovative companies such as for instance Skyryse* with complex aircraft flight controls.
- Built Robotics in the construction.
- Path Robotics in manufacturing.
- Caterpillar in mining.
- Blue River in agriculture.
- Saildrone in ocean exploration.
- Simbe Robotics* in retail.
- Intuitive Surgical in healthcare.
Robot solutions that share many key dimensions:
- Introduce advanced level levels of automation or autonomy that can pair with its human operator.
- Deliver at the least two of the three value dimensions: safer operation, improved cost of operation, high total utilization of assets.
- Shift the operators’ time to higher-value tasks; eventually to manage across multiple functions in parallel.
- Primarily software-defined across both get a handle on and perception systems.
- Easily retrofit into customers’ assets base at price points significantly less than 20% of the cost of the underlying asset.
- Can go to market ‘as a service’ with recurring revenue and healthy margins.
Technology has empowered humankind to manage to the impossible.
The impossible means we can make more complex decisions at orders of magnitude more precision and speed. Yet so many industries still depend on human labor and operations over human ingenuity and authority.
As the planet adapts to social distancing and remote work, it’s more essential than ever to leverage technology as our proverbial exoskeletons to maximize what humans are great at, and let technology do the rest.
*Venrock can be an investor in Skyryse and Simbe Robotics