Getting the Ball Rolling: Learning a Dexterous Policy for a Biomimetic
  Tendon-Driven Hand with Rolling Contact Joints
Biomimetic, dexterous robotic hands have the potential to replicate much of
the tasks that a human can do, and to achieve status as a general manipulation
platform. Recent advances in reinforcement learning (RL) frameworks have
achieved remarkable performance in quadrupedal locomotion and dexterous
manipulation tasks. Combined with GPU-based highly parallelized simulations
capable of simulating thousands of robots in parallel, RL-based controllers
have become more scalable and approachable. However, in order to bring
RL-trained policies to the real world, we require training frameworks that
output policies that can work with physical actuators and sensors as well as a
hardware platform that can be manufactured with accessible materials yet is
robust enough to run interactive policies. This work introduces the biomimetic
tendon-driven Faive Hand and its system architecture, which uses tendon-driven
rolling contact joints to achieve a 3D printable, robust high-DoF hand design.
We model each element of the hand and integrate it into a GPU simulation
environment to train a policy with RL, and achieve zero-shot transfer of a
dexterous in-hand sphere rotation skill to the physical robot hand.
64d06dcd59503263d9ff4f15
Yasunori Toshimitsu
admin_assigned
2023-08-07T12:58:45.635Z
64d0b5ab5de9e1e911b7f468
/avatars/5f9b8ee474f8bdd12a53c2ea40a8b405.svg
Yasunori Toshimitsu
user
JeSuisYasu
64d06dcd59503263d9ff4f16
Benedek Forrai
extracted_confirmed
2023-08-07T14:28:10.783Z
64d0ff507d8cc9f0704438b8
/avatars/ff40934d8fde4cc385f134e16a63a04a.svg
Benedek Forrai
user
bforrai
64d06dcd59503263d9ff4f17
Barnabas Gavin Cangan
extracted_confirmed
2023-08-07T13:05:19.568Z
64d0eb7e5de9e1e911beac19
https://cdn-avatars.huggingface.co/v1/production/uploads/64d0eb7e5de9e1e911beac19/tnT8EK3a6Nm7nhc0n23hh.jpeg
Barnabas Gavin Cangan
user
bgavin
64d06dcd59503263d9ff4f18
Ulrich Steger
64d06dcd59503263d9ff4f19
Manuel Knecht
claimed_verified
2023-08-08T01:19:41.008Z
64d112015c86caf95115acfb
/avatars/9c0960aa38e88433d686952f13564a84.svg
Manuel Knecht
user
knechtma
64d06dcd59503263d9ff4f1a
Stefan Weirich
64d06dcd59503263d9ff4f1b
Robert K. Katzschmann
extracted_confirmed
2023-08-07T14:59:49.823Z
64d106a2e8df1f66dda6f690
/avatars/e127356769607563ff2775aaf4341e2d.svg
Robert K. Katzschmann
user
katzschmann
Getting the Ball Rolling: Learning a Dexterous Policy for a Biomimetic
  Tendon-Driven Hand with Rolling Contact Joints
Biomimetic, dexterous robotic hands have the potential to replicate much of
the tasks that a human can do, and to achieve status as a general manipulation
platform. Recent advances in reinforcement learning (RL) frameworks have
achieved remarkable performance in quadrupedal locomotion and dexterous
manipulation tasks. Combined with GPU-based highly parallelized simulations
capable of simulating thousands of robots in parallel, RL-based controllers
have become more scalable and approachable. However, in order to bring
RL-trained policies to the real world, we require training frameworks that
output policies that can work with physical actuators and sensors as well as a
hardware platform that can be manufactured with accessible materials yet is
robust enough to run interactive policies. This work introduces the biomimetic
tendon-driven Faive Hand and its system architecture, which uses tendon-driven
rolling contact joints to achieve a 3D printable, robust high-DoF hand design.
We model each element of the hand and integrate it into a GPU simulation
environment to train a policy with RL, and achieve zero-shot transfer of a
dexterous in-hand sphere rotation skill to the physical robot hand.
The tendon-driven Faive Hand, a biomimetic robotic hand, leverages reinforcement learning for zero-shot transfer of dexterous manipulation skills from simulation to physical hardware.
tendon-driven rolling contact joints GPU simulation reinforcement learning zero-shot transfer in-hand sphere rotation dexterous manipulation