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Dexterous Robotic Manipulation using Deep Reinforcement Learning and Knowledge Transfer for Complex Sparse Reward-based Tasks. (arXiv:2205.09683v1 [cs.RO])
May 20, 2022, 1:12 a.m. | Qiang Wang, Francisco Roldan Sanchez, Robert McCarthy, David Cordova Bulens, Kevin McGuinness, Noel O'Connor, Manuel Wüthrich, Felix Widmaie
cs.LG updates on arXiv.org arxiv.org
This paper describes a deep reinforcement learning (DRL) approach that won
Phase 1 of the Real Robot Challenge (RRC) 2021, and then extends this method to
a more difficult manipulation task. The RRC consisted of using a TriFinger
robot to manipulate a cube along a specified positional trajectory, but with no
requirement for the cube to have any specific orientation. We used a relatively
simple reward function, a combination of goal-based sparse reward and distance
reward, in conjunction with Hindsight …
arxiv knowledge learning reinforcement reinforcement learning transfer
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