First, the robot has a simpler time learning the layout of the home—it doesn’t need to learn complex paths around obstacles to get from one place to another because there’s a kitchen island or a new recliner to get in its way. That also means the robot won’t get under foot as someone tries to navigate around their own home. TRI’s bot can fold up tightly to the ceiling when it’s not in use, so it effectively takes up zero usable space.
TRI also claims that the top-down viewpoint gives the machine a better point of view for observing its own actions and the relative position of different objects it might have to manipulate. Toyota is big on robots learning from limited sets of parameters. So, if you wanted the robot to learn to wipe down the countertops, a human could perform that action in VR and the robot would understand the human’s actions and mimic them in the real world while monitoring variables. If you wanted to teach the robot to clean your countertops, you could do it in VR and it would understand going forward. Also, through what’s known as fleet learning, a human could teach one bot, and other networked machines would get the same lessons.
Numerous joints in the robotic arm allow it to move in all directions. In fact, some of the joints are redundant, allowing it to rotate and orient itself in pretty much any position. The problem, however, is that the whole house needs to essentially be designed around the machine. Installing the complicated support system as an aftermarket product would be a huge undertaking (though, Toyota currently has no concrete plans to bring this to market yet anyway). Ideally, the robot would be a central part of the home’s planning before construction gets underway.
That concept makes more sense as it scales to multi-unit buildings like elder care facilities. If the builders could make the units look nearly identical on the inside, engineers could train one robot, then share that information with the rest of the networked machines.
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