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The problem is that even a specialist vacuuming robot needs to be more of a generalist than you realize. For example, take the "nothing was allowed to be on the floor" restriction. To relax this restriction, the robot needs to know what it can and cannot do for something it sees in the floor. List everything that could be on the floor (I'll wait). The robot needs to recognize all of these things, and know the correct behavior for each. You could still do this! You'd need to label a ton of items and hardcode a bunch of behaviors. After all this R&D your robot vacuum would need to cost $5k-$10k, and you'd wish you'd worked on a higher priced product like a robotic forklift instead. Still, it's feasible to build this. However, manipulation is a few orders of magnitude more complex than navigation. You have to recognize many objects, their precise poses, and many aspects of the objects. Think about opening a can with a can opener. The robot needs to recognize a few parts on the can opener, and how it fits on the can. Then you've got to hardcode behaviors for attaching the opener and then turning the knob until done, and removing the lid. Doable, but very very hard. This is feasible, and you can build a can opening robot, but after 9 months of R&D, that's all your specialized robot will be able to do, and oops, there's 40 more tasks it needs to accomplish to cook a dinner. The only way to build this product is to tackle all the tasks at once, and that's why this research is so important. Everything you want a robot to do needs O(dozens) of individual tasks, and when each task takes O(year) to build it's impossible to finish. |