title: Getting the Ball Rolling: Learning a Dexterous Policy for a Biomimetic
    Tendon-Driven Hand with Rolling Contact Joints
summary: 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.
paper_authors._id: 64d06dcd59503263d9ff4f15
paper_authors.name: Yasunori Toshimitsu
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paper_authors.fullname: Yasunori Toshimitsu
paper_authors.type: user
paper_authors.user: JeSuisYasu
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paper_authors.name: Benedek Forrai
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paper_authors.fullname: Benedek Forrai
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paper_authors.user: bforrai
paper_authors._id: 64d06dcd59503263d9ff4f17
paper_authors.name: Barnabas Gavin Cangan
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paper_authors.fullname: Barnabas Gavin Cangan
paper_authors.type: user
paper_authors.user: bgavin
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paper_authors.name: Ulrich Steger
paper_authors._id: 64d06dcd59503263d9ff4f19
paper_authors.name: Manuel Knecht
paper_authors.status: claimed_verified
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paper_authors._id: 64d112015c86caf95115acfb
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paper_authors.fullname: Manuel Knecht
paper_authors.type: user
paper_authors.user: knechtma
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paper_authors.name: Stefan Weirich
paper_authors._id: 64d06dcd59503263d9ff4f1b
paper_authors.name: Robert K. Katzschmann
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2023-08-07T14:59:49.823Z
paper_authors._id: 64d106a2e8df1f66dda6f690
paper_authors.avatarUrl: /avatars/e127356769607563ff2775aaf4341e2d.svg
paper_authors.fullname: Robert K. Katzschmann
paper_authors.type: user
paper_authors.user: katzschmann
paper_title: Getting the Ball Rolling: Learning a Dexterous Policy for a Biomimetic
    Tendon-Driven Hand with Rolling Contact Joints
paper_summary: 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.
paper_ai_keywords: reinforcement learning (RL) quadrupedal locomotion dexterous manipulation tasks GPU-based highly parallelized neighborhoods RL-based controllers tendon-driven Faive Hand tendon-driven rolling contact joints 3D printable hand design GPU simulation environment zero-shot transfer