Robots can be said to have capabilities that roughly map into three categories:
Locomotion — traversing environments, typically to carry a payload. Manipulation — affecting objects in the environment. Perception — making observations about the environment.
Modern robotics combine these capabilities, allowing them to perform more complex tasks in challenging and dynamic environments, but for the sake of clarity, let us imagine the simplest and purest instantiations of each:
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A simple locomotion robot will move from A to B on command, without needing to perceive the world or affect its surroundings. See: an elevator.
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A simple manipulation robot will affect an object in the environment without having to perceive the object, or having to move to find the object. See: a mechanized hammer.
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A simple perception robot will need to observe the environment, without having to manipulate the environment or transport itself to perform its task: See: a people-counting CCTV camera.
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Correspondingly, we can think of categories of tasks in the world that map quite well to these capabilities: there are locomotion tasks, manipulation tasks, and perception tasks.
Tesla cars are locomotion robots.
Weave laundry folders are manipulation robots.
Kabam mall security rovers are perception robots.
Tesla cars obviously rely heavily on perception, but they do so in order to perform a locomotion task, just as Weave’s robots rely on perception in order to perform a manipulation task, and Kabam relies on locomotion capabilities in order to perform a perception task.
Today, almost all capital and attention in robotics is going towards perfecting robots for locomotion and manipulation tasks - but I want to show you that this is almost certainly a strategic error.
When considering a robot for a task, there are three helpful dimensions of analysis to help us determine if the robot is right for the job:
Value - Can the robot perform the job cheaper or better? Cost - How much will it cost to develop, deploy and maintain the robot? Risk - How disruptive would it be if the robot malfunctioned or failed? How much uptime can you guarantee, and how catastrophic are the failure modes?
The challenge in finding good targets for deployment is finding where you can either perform a job cheaper or better, where the cost of getting started isn’t prohibitive, and the risk of failure isn’t too costly.
For a manipulation example, it is very costly when a robot on an auto assembly line malfunctions, because the car might be damaged - or the whole assembly line might stop until the issue has been addressed. The riskier the environment is, the more 9s of reliability you have to be able to guarantee the customer to get your foot in the door and to scale.
Looking at value and cost together gives us a sense of time to ROI, and risk gives us a sense of how much reliability we need before the deployment is viable at all. There is often a brutal reliability cliff that must be overcome before the robots can come out of pilot stage.