That's a nice piece of motor engineering. It's well known that high ratio gearboxes for robots are a headache. Back driveability doesn't work, and tiny teeth are fragile. Comments on this go all the way back to Feynman writing about his time spent engineering automatic gunnery aiming systems in WWII.

This new discovery is that gearbox problems mess up a machine learning system. It's trying to track gearbox noise and is using up all its learning capacity on that. This discovery means that robotics people can tap machine learning funding for motor and gearbox development. Robotics labs used to be really low-budget operations. No longer.

What you really want is a direct drive motor, but those have to be large-diameter. They can be flat; that's a pancake motor. That's too large for fingers. So their compromise moves partly in that direction; the rotor is flatter, torques are higher, speeds are slower, and gearbox ratios are lower. As they point out, reflected inertia is the square of the gear ratio, because the gear ratio gets you both going out and coming back. So this is a bigger than linear win.

Good back-drivabiilty means much less risk of gear breakage on overload. Some of the academic designs, such as harmonic drives and series elastic actuators, have huge gear ratios in a small space. That's OK for prototypes but not production. As I've mentioned before, "you cannot strip the teeth of a magnetic field", a line from a GE electric locomotive salesman around 1900. If an overload forces a motor backwards, nothing breaks.

Would have been nice to hear more about the motor design. That's the real achievement here. There are CAD tools which understand electromagnetic fields now, so strange motor geometries are not as much of a trial and error and experience process as it once was. It's also respectable for an EE to work on rotating machinery again. That field matured around the 1960s, and until computers took over motor control, didn't change much.

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AceJohnny2 an hour ago | parent [-]

(I am not a mechanical engineer) Are capstan drives a solution to some of these gearing problems?

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Animats an hour ago | parent [-]

They don't back-drive well. The whole point of this hand design is to back-drive the contact forces into the motor, where there's force control. They're somewhat bulky, too.

Key concept: force-based motor control works quite well. Preserve that property through the gear train and force-based hand control works.

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LoganDark an hour ago | parent [-]

> They don't back-drive well

What? An ideal capstan drive can be backdriven perfectly fine. You only run into problems once it stops being ideal (e.g. built out of heavy parts, high gear ratio, etc.)

	▲	Animats 32 minutes ago \| parent [-]
		It's the high reduction ratio that's the problem. If you build a 200:1 capstan, it's not going to back drive well. And it won't be anywhere near ideal.