Getting the Ball Rolling: Learning a Dexterous Policy for a Biomimetic
  Tendon-Driven Hand with Rolling Contact Joints
Authors: name: Yasunori Toshimitsu, name: Benedek Forrai, name: Barnabas Gavin Cangan, name: Ulrich Steger, name: Manuel Knecht, name: Stefan Weirich, name: Robert K. Katzschmann

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.