The framework designs both high-DOF generalist and low-DOF specialized hands
Learning to design a robot hand from human videos.
If the end goal of robot hands is to perform human motion, then we should optimize the hardware design with human motion - and on a large scale! We can generate both a high-dof generalist hand, and also low-dof specialized hands from human demonstration.
Many users express enthusiasm for optimizing robot hands from large-scale human motion data because the resulting 3D-printed designs are fun to play with and the work comes across as interesting.
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Paper: https://arxiv.org/pdf/2606.20549 Webpage: https://yswhynot.github.io/generating-robot-hands/
This was quite a weird project when I started a year ago🦭 but the designs that came out of 3d printers are so fun to play with!
If the end goal of robot hands is to perform human motion, then we should optimize the hardware design with human motion - and on a large scale! We can generate both a high-dof generalist hand, and also low-dof specialized hands from human demonstration.
@yswhynot Interesting work! Are there any reasons to design it to a 2-finger gripper style instead of dex hand style?
with my awesome co-authors @ncklashansen @baicrystal25 @carlo_sferrazza Mike Tolley @xiaolonw
We also find that by simply making the hardware optimizable with control policy, the fingertip tracking error can go down to less than 1mm - not possible for the commercial robot hands we tried of the same dof
@BaoyuLi6 Good question🙌 One reason is we noticed many human demos are dominate by these two fingers. Another more realistic reason is more fingers lead to a lot more collisions and hard to manufacture, so we just start with an easier problem :)
