Title: Learning Dexterous In-Hand Manipulation,
Abstract: We use reinforcement learning (RL) to learn dexterous in-hand manipulation
policies which can perform vision-based object reorientation on a physical
Shadow Dexterous Hand. The training is performed in a simulated environment in
which we randomize many of the physical properties of the system like friction
coefficients and an object's appearance. Our policies transfer to the physical
robot despite being trained entirely in simulation. Our method does not rely on
any human demonstrations, but many behaviors found in human manipulation emerge
naturally, including finger gaiting, multi-finger coordination, and the
controlled use of gravity. Our results were obtained using the same distributed
RL system that was used to train OpenAI Five. We also include a video of our
results: this https URL