Learning to Transfer In-Hand Manipulations Using a Greedy Shape Curriculum

• URL: http://arxiv.org/abs/2303.12726v1
• Date: Tue, 14 Mar 2023 17:08:19 GMT
• Title: Learning to Transfer In-Hand Manipulations Using a Greedy Shape Curriculum
• Authors: Yunbo Zhang, Alexander Clegg, Sehoon Ha, Greg Turk, Yuting Ye
• Abstract summary: We show that natural and robust in-hand manipulation of simple objects in a dynamic simulation can be learned from a high quality motion capture example. We propose a simple greedy curriculum search algorithm that can successfully apply to a range of objects such as a teapot, bunny, bottle, train, and elephant.
• Score: 79.6027464700869
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: In-hand object manipulation is challenging to simulate due to complex contact dynamics, non-repetitive finger gaits, and the need to indirectly control unactuated objects. Further adapting a successful manipulation skill to new objects with different shapes and physical properties is a similarly challenging problem. In this work, we show that natural and robust in-hand manipulation of simple objects in a dynamic simulation can be learned from a high quality motion capture example via deep reinforcement learning with careful designs of the imitation learning problem. We apply our approach on both single-handed and two-handed dexterous manipulations of diverse object shapes and motions. We then demonstrate further adaptation of the example motion to a more complex shape through curriculum learning on intermediate shapes morphed between the source and target object. While a naive curriculum of progressive morphs often falls short, we propose a simple greedy curriculum search algorithm that can successfully apply to a range of objects such as a teapot, bunny, bottle, train, and elephant.

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