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A little weekend side-project, trying out gravity comp with PWM controls on an so-101 It holds but feels very stiff, the giant gear ratios probably impact backdriveability. Will see if any AI/SW tricks can mitigate, would prefer keeping standard servos Pepijn 🦾

Ville🤖

3,519 subscribers

16,106 views • 2 months ago •via X (Twitter)

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11 Comments

Jason Liu2 months ago

@pepijn2233 stiction compensation can help a lot if you haven’t tried it already

Embedr2 months ago

@pepijn2233 that limb is looking dangerously close to gaining free will

Paolo AI2 months ago

@pepijn2233 Let's compare notes I did the same for the ElRobot arm.

Ville🤖2 months ago

@pepijn2233 > "these motors only have position control" this is not right for feetechs, they offer direct pwm control as well which allows us to approximate voltage & current control. this is the correct way to implement gravity comp rather than using position control

Paolo AI2 months ago

@pepijn2233 I like your approach with PWM I am going to test it too.

atharva ☆2 months ago

@pepijn2233 share code I think I can help you with this

Ville🤖2 months ago

@pepijn2233 I will eventually but it's not ready yet, what part might you be able to help out with?

Praveen Selvaraj2 months ago

@pepijn2233 should make teleop easier right, esp bimanual ?

Ville🤖2 months ago

@pepijn2233 it's step 1/n on that journey and I'm not sure if all steps are feasible on these motors

Makr3D2 months ago

@pepijn2233 The standard servos may still be workable if the gravity term stays deliberately conservative. Have you measured backdrive torque across the joint range yet? That would help separate gearbox friction from controller stiffness before adding more software compensation.

Yuv Bindal1 month ago

@pepijn2233 gravity comp on a so101 is fiddly. connect sometime

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Epoch AI researcher, Greg Burnham on the board game his team is using to prove AI still can't learn from experience like humans do: "We used the board game Earthborne Rangers as a test case for, if we give AI something unfamiliar, can it get better than its starting point?" "We chose a board game that can be played in solo mode, has relatively little spatial or visual reasoning. This game is based on building a deck and playing through cards, and you're exploring a wilderness landscape. Our benchmark covers five days of this game." "Out of the box, AI comes out pretty strong. It's probably playing better than a human would on a human's first try. But then we say, okay, keep playing. Take notes as you go. You're going to try to get better." "We basically just see flat performance. They don't make friends with the board game and figure out how it works and get better at it over time." "There's 21 objectives. Our best human run ever hits 20 out of 21. The AI systems can get 10, 11, 12 on an early run, but we let them play like 30 times, and they don't get any better than that." "This is a result more generally about learning on the fly. People call this continual learning. It's a very strong capability. If AI ever develops it, it kind of really upends a lot of assumptions we have. But so far this seems to not be a capability that we're observing." Greg Burnham Epoch AI

Epoch AI researcher, Greg Burnham on the board game his team is using to prove AI still can't learn from experience like humans do: "We used the board game Earthborne Rangers as a test case for, if we give AI something unfamiliar, can it get better than its starting point?" "We chose a board game that can be played in solo mode, has relatively little spatial or visual reasoning. This game is based on building a deck and playing through cards, and you're exploring a wilderness landscape. Our benchmark covers five days of this game." "Out of the box, AI comes out pretty strong. It's probably playing better than a human would on a human's first try. But then we say, okay, keep playing. Take notes as you go. You're going to try to get better." "We basically just see flat performance. They don't make friends with the board game and figure out how it works and get better at it over time." "There's 21 objectives. Our best human run ever hits 20 out of 21. The AI systems can get 10, 11, 12 on an early run, but we let them play like 30 times, and they don't get any better than that." "This is a result more generally about learning on the fly. People call this continual learning. It's a very strong capability. If AI ever develops it, it kind of really upends a lot of assumptions we have. But so far this seems to not be a capability that we're observing." Greg Burnham Epoch AI

MTS

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