I spent some time on legged locomotion back in the 1990s. It was clear then that you wanted torque control, and I did some work on the theory for that, trying to solve it from first principles, not machine learning. Got some nice theory and a patent out. But the parts just weren't there to build such things. As the article points out, the key to this is motor back-drivability. The final drive has to survive shock loads, and it has to dump forces into the motor, where the magnetic fields can take it. As I've quoted before, "you cannot strip the teeth of a magnetic field", a comment from early General Electric locomotive sales. (Locomotives are Diesel-electric, not Diesel with a clutch and shifting gearbox, because the clutch required is huge. Yes, it's been tried.)
That's something few areas of engineering cared about, with the exception of aircraft flight control systems with mechanical backup.
Pneumatic actuators looked promising, but proportional dynamic valves were big, heavy, and about $1000 each. Linear motors (not ball screws) looked like the coming thing back then, as 10:1 power/weight ratio had been achieved.
But that technology never got much further, and Aura, the biggest player, collapsed in a financial scandal. Series elastic actuators were (and still are) a race between the spring compressing and the ball screw motor starting up. Hydraulics were too clunky; Boston Dynamics built a 400 pound mule, but the Diesel power pack never worked.
Direct drive pancake motors were used by some SCARA industrial robots, but those were too big for leg joints.
I thought someone would crack the direct drive problem eventually, but nobody ever did. We're still stuck with some gear reduction.
Some of the exotic ideas for muscles mentioned in this article go back that far. The McKinney muscle is old, and not too useful. There was some interest in electrorheological fluids, fluids whose mechanical properties change when an electric field is applied. That didn't become useful either. Shape-memory alloys were a dead end; liquid cooling can overcome the slowness problem, but not the inefficiency problem. Everybody went back to good old electric motors, although they became 3-phase AC instead of DC. It helped that the drone industry made 3-phase motors and their controllers small, cheap, and powerful.
Academic robotics groups were tiny. MIT and Stanford had less than a dozen people each.
Progress required hundreds of millions of dollars for all that custom engineering and R&D. The level of effort just wasn't there. Nor would throwing money at the problem prior to machine learning have led to useful products.
It's impressive what's been accomplished in the last five years. It took a lot of money.
AI was clearly heavily used in the making of this article, and I almost dismissed it as slop. But after reading it I think there's enough correct information here for it to be useful as a general overview of the problems in the space.
The power of money.
I spent some time on legged locomotion back in the 1990s. It was clear then that you wanted torque control, and I did some work on the theory for that, trying to solve it from first principles, not machine learning. Got some nice theory and a patent out. But the parts just weren't there to build such things. As the article points out, the key to this is motor back-drivability. The final drive has to survive shock loads, and it has to dump forces into the motor, where the magnetic fields can take it. As I've quoted before, "you cannot strip the teeth of a magnetic field", a comment from early General Electric locomotive sales. (Locomotives are Diesel-electric, not Diesel with a clutch and shifting gearbox, because the clutch required is huge. Yes, it's been tried.) That's something few areas of engineering cared about, with the exception of aircraft flight control systems with mechanical backup.
Pneumatic actuators looked promising, but proportional dynamic valves were big, heavy, and about $1000 each. Linear motors (not ball screws) looked like the coming thing back then, as 10:1 power/weight ratio had been achieved. But that technology never got much further, and Aura, the biggest player, collapsed in a financial scandal. Series elastic actuators were (and still are) a race between the spring compressing and the ball screw motor starting up. Hydraulics were too clunky; Boston Dynamics built a 400 pound mule, but the Diesel power pack never worked. Direct drive pancake motors were used by some SCARA industrial robots, but those were too big for leg joints. I thought someone would crack the direct drive problem eventually, but nobody ever did. We're still stuck with some gear reduction.
Some of the exotic ideas for muscles mentioned in this article go back that far. The McKinney muscle is old, and not too useful. There was some interest in electrorheological fluids, fluids whose mechanical properties change when an electric field is applied. That didn't become useful either. Shape-memory alloys were a dead end; liquid cooling can overcome the slowness problem, but not the inefficiency problem. Everybody went back to good old electric motors, although they became 3-phase AC instead of DC. It helped that the drone industry made 3-phase motors and their controllers small, cheap, and powerful.
Academic robotics groups were tiny. MIT and Stanford had less than a dozen people each. Progress required hundreds of millions of dollars for all that custom engineering and R&D. The level of effort just wasn't there. Nor would throwing money at the problem prior to machine learning have led to useful products.
It's impressive what's been accomplished in the last five years. It took a lot of money.
Except we don't need 100% bipedal robots. Wheels are perfectly ok for majority of city work and factory floor.
Put the robot on rollerskates break the wheels for the occasional stair.
Why the AI "engineering expert"? Seems to take some credibility away from what otherwise could be an interesting and informative read.
Yeah I couldn't get past so many issues in the AI generated illustrations. Not useful at all when they are completely wrong.
Check out opensource actuator for robots.
Opentorque actuator
https://www.gabrael.io/new-page
https://github.com/G-Levine/OpenTorque-Actuator
AI was clearly heavily used in the making of this article, and I almost dismissed it as slop. But after reading it I think there's enough correct information here for it to be useful as a general overview of the problems in the space.