期刊
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
卷 39, 期 1, 页码 3-20出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0278364919887447
关键词
Dexterous manipulation; multifingered hands; adaptive control; learning and adaptive systems; humanoid robots
类别
We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies that 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 such as 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: .
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