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ACCount39 9 hours ago [-]
It's impressive that something this simple can do this much. But those funky types of actuators all live and die by transfer learning now.
If a robot AI can figure out how to operate them with very little sim and teleop data, and learn to take advantage of their strengths while maintaining good performance on tasks learned from UMI datasets, teleop data or human headcam videos? Allowing the same "robot mind" to work with different actuators?
Then I would expect those to have a decent niche - sitting between the classic two finger UMI gripper and a humanoid hand. Not a drop-in replacement for a human hand, but still more dexterity per hand without sacrificing all of the ruggedness and mechanical simplicity.
If transfer learning for different actuator types doesn't work so well? I expect the field to collapse to a binary of "UMI gripper or humanoid hand", with nearly no in-between.
In general, I'm carefully optimistic? But we are yet to demonstrate with confidence that this kind of transfer for actuators with radically different kinematics would work.
measurablefunc 8 hours ago [-]
Should be doable w/ existing AIs to generate a staged sequence of operations by taking a demonstration & deriving another sequence of operations for achieving the same outcome w/ another set of actuators. I'm not a roboticist but robotic arms have specifications & those specifications are basically generalized algebraic datatypes so Astra or even Grok should be able to generate programs for manipulating most objects.
Animats 15 hours ago [-]
It works best on problems which are strongly Cartesian. The chopsticks demo shows the limitations of this. The gripper can pick up two chopsticks, but it can't do much with them, because it can't rotate them to bring them together at the points.
But it seems to be good for lab equipment with simple geometry.
Also, where are the motors? Inside the gripper, or at the other end of cables?
octoberfranklin 14 hours ago [-]
Rotational screw-on caps are not Cartesian, and this excels at dealing with them.
I'm very impressed with this.
Re: chopsticks, oh but it can! It can roll one chopstick between one pair of grippers. Do that while holding the tip against a fixed object and you'll tilt the chopstick. Once you get the tips touching, it can use them.
The motors are inside those chonky things just outside the fingertips. I'm guessing hobby servos, which are tiny cheap and strong.
varjag 13 hours ago [-]
Still there are fundamental limitations. Holding a bowl full of liquid, a heavy jug with a rounded handle etc.
octoberfranklin 12 hours ago [-]
What would be the problem with a bowl of liquid?
The balancing there comes from the wrist joint, not the fingers. Obviously this device is supposed to be mounted on a wrist joint that can rotate (pitch+yaw). Wrist joints are pretty well-understood these days.
Rounded handle I don't see the problem unless the hole is too small for one of the fingers.
varjag 11 hours ago [-]
Holding a rounded handle with a rectangular profile slider is inherently unstable as it's extremely sensitive to the pinch point.
And the bowl has a radius that make orthogonal grip impractical.
octoberfranklin 6 hours ago [-]
But it has two pinch points. There are two separate grippers. Look at how it uses scissors.
If the radius of the bowl is that big it's too big for anything one-handed (with a reasonable size hand). So use two hands, like people do.
Aardwolf 2 hours ago [-]
I feel like there could be a fifth finger at the top 90 degrees rotated compared to the others, to e.g. click a pen
One of the challenging parts of imitating human hands is that our hands sense through the same surface which deforms as we bend our fingers. We don't have good robotic devices that can do that.
This sidesteps the problem: the surface that contacts the object is flat and never bends, so you can apply a huge variety of very detailed grid sensors to it.
The "rolling between the fingers" trick is ultimately what eliminates the need for deformation.
If a robot AI can figure out how to operate them with very little sim and teleop data, and learn to take advantage of their strengths while maintaining good performance on tasks learned from UMI datasets, teleop data or human headcam videos? Allowing the same "robot mind" to work with different actuators?
Then I would expect those to have a decent niche - sitting between the classic two finger UMI gripper and a humanoid hand. Not a drop-in replacement for a human hand, but still more dexterity per hand without sacrificing all of the ruggedness and mechanical simplicity.
If transfer learning for different actuator types doesn't work so well? I expect the field to collapse to a binary of "UMI gripper or humanoid hand", with nearly no in-between.
In general, I'm carefully optimistic? But we are yet to demonstrate with confidence that this kind of transfer for actuators with radically different kinematics would work.
Also, where are the motors? Inside the gripper, or at the other end of cables?
I'm very impressed with this.
Re: chopsticks, oh but it can! It can roll one chopstick between one pair of grippers. Do that while holding the tip against a fixed object and you'll tilt the chopstick. Once you get the tips touching, it can use them.
The motors are inside those chonky things just outside the fingertips. I'm guessing hobby servos, which are tiny cheap and strong.
The balancing there comes from the wrist joint, not the fingers. Obviously this device is supposed to be mounted on a wrist joint that can rotate (pitch+yaw). Wrist joints are pretty well-understood these days.
Rounded handle I don't see the problem unless the hole is too small for one of the fingers.
And the bowl has a radius that make orthogonal grip impractical.
If the radius of the bowl is that big it's too big for anything one-handed (with a reasonable size hand). So use two hands, like people do.
This sidesteps the problem: the surface that contacts the object is flat and never bends, so you can apply a huge variety of very detailed grid sensors to it.
The "rolling between the fingers" trick is ultimately what eliminates the need for deformation.