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Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost
[article]
2018
arXiv
pre-print
Dexterous multi-fingered robotic hands can perform a wide range of manipulation skills, making them an appealing component for general-purpose robotic manipulators. However, such hands pose a major challenge for autonomous control, due to the high dimensionality of their configuration space and complex intermittent contact interactions. In this work, we propose deep reinforcement learning (deep RL) as a scalable solution for learning complex, contact rich behaviors with multi-fingered hands.
arXiv:1810.06045v1
fatcat:h5dm7rvxj5h5lgkihznx6yelhu