Part of it is not the difficulty of the task, but many of the tasks you give as examples require very expensive hand built (ironically) robotics hardware to even try them. There are mere hundreds of instances of that hardware, and they are hundreds of thousands of dollars each.
There is insufficient scale. Think of all the AI hype and weak results before labs had clusters of 2048 A100s and trillion token text databases. Scale counts for everything. If in 1880, chemists had figured out how to release energy through fission, but didn’t have enough equipment and money to get weapons grade fissionables until 1944, imagine how bored we would have been with nuclear bomb hype. Nature does not care if you know the answer, only that you have more than a kilogram of refined fissionables, or nothing interesting will happen.
The thing is about your examples is that machines are trivially superhuman in all those tasks. Sure, not at the full set combined, but that’s from lack of trying—nobody has built anything with the necessary scale.
I am sure you have seen the demonstrations of a ball bearing on a rail and an electric motor keeping it balanced, or a double pendulum stabilized by a robot, or quadcopters remaining in flight with 1 wing clipped, using a control algorithm that dynamically adjusts flight after the wing damage.
All easy RL problems, all completely impossible for human beings. (we react too slowly)
The majority of what you mention are straightforward reinforcement learning problems and solvable with a general method. Most robotics manipulation tasks fall into this space.
Note that there is no economic incentive to solve many of the tasks you mention, so they won’t be. But general manufacturing robotics, where you can empty a bin of random parts in front of the machine(s), and they assemble as many fully built products of the design you provided that the parts pile allows? Very solvable and the recent google AI papers show it’s relatively easy. (I say easy because the solutions are not very complex in source code, and relatively small numbers of people are working on them.)
I assume at least for now, everyone will use nice precise industrial robot arms and overhead cameras and lidars mounted in optimal places to view the work space—there is no economic benefit to ‘embodiment’ or a robot janitor entering a building like you describe. Dancing with a partner is too risky.
But it’s not a problem of motion control or sensing, machinery is superhuman in all these ways. It’s a waste of components and compute to give a machine 2 legs or that many DOF. Nobody is going to do that for a while.
Part of it is not the difficulty of the task, but many of the tasks you give as examples require very expensive hand built (ironically) robotics hardware to even try them. There are mere hundreds of instances of that hardware, and they are hundreds of thousands of dollars each.
There is insufficient scale. Think of all the AI hype and weak results before labs had clusters of 2048 A100s and trillion token text databases. Scale counts for everything. If in 1880, chemists had figured out how to release energy through fission, but didn’t have enough equipment and money to get weapons grade fissionables until 1944, imagine how bored we would have been with nuclear bomb hype. Nature does not care if you know the answer, only that you have more than a kilogram of refined fissionables, or nothing interesting will happen.
The thing is about your examples is that machines are trivially superhuman in all those tasks. Sure, not at the full set combined, but that’s from lack of trying—nobody has built anything with the necessary scale.
I am sure you have seen the demonstrations of a ball bearing on a rail and an electric motor keeping it balanced, or a double pendulum stabilized by a robot, or quadcopters remaining in flight with 1 wing clipped, using a control algorithm that dynamically adjusts flight after the wing damage.
All easy RL problems, all completely impossible for human beings. (we react too slowly)
The majority of what you mention are straightforward reinforcement learning problems and solvable with a general method. Most robotics manipulation tasks fall into this space.
Note that there is no economic incentive to solve many of the tasks you mention, so they won’t be. But general manufacturing robotics, where you can empty a bin of random parts in front of the machine(s), and they assemble as many fully built products of the design you provided that the parts pile allows? Very solvable and the recent google AI papers show it’s relatively easy. (I say easy because the solutions are not very complex in source code, and relatively small numbers of people are working on them.)
I assume at least for now, everyone will use nice precise industrial robot arms and overhead cameras and lidars mounted in optimal places to view the work space—there is no economic benefit to ‘embodiment’ or a robot janitor entering a building like you describe. Dancing with a partner is too risky.
But it’s not a problem of motion control or sensing, machinery is superhuman in all these ways. It’s a waste of components and compute to give a machine 2 legs or that many DOF. Nobody is going to do that for a while.