On the Feasibility of Learning Finger-gaiting In-hand Manipulation with Intrinsic Sensing

• URL: http://arxiv.org/abs/2109.12720v1
• Date: Sun, 26 Sep 2021 23:22:29 GMT
• Title: On the Feasibility of Learning Finger-gaiting In-hand Manipulation with Intrinsic Sensing
• Authors: Gagan Khandate, Maxmillian Haas-Heger, Matei Ciocarlie
• Abstract summary: We use model-free reinforcement learning to learn finger-gaiting only via precision grasps. To tackle the inherent instability of precision grasping, we propose the use of initial state distributions. Our method can learn finger-gaiting with significantly improved sample complexity than the state-of-the-art.
• Score: 0.7373617024876725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Finger-gaiting manipulation is an important skill to achieve large-angle in-hand re-orientation of objects. However, achieving these gaits with arbitrary orientations of the hand is challenging due to the unstable nature of the task. In this work, we use model-free reinforcement learning (RL) to learn finger-gaiting only via precision grasps and demonstrate finger-gaiting for rotation about an axis purely using on-board proprioceptive and tactile feedback. To tackle the inherent instability of precision grasping, we propose the use of initial state distributions that enable effective exploration of the state space. Our method can learn finger-gaiting with significantly improved sample complexity than the state-of-the-art. The policies we obtain are robust and also transfer to novel objects.

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