Differentiable Simulation of Soft Multi-body Systems

May 03, 2022 ยท Declared Dead ยท ๐Ÿ› Neural Information Processing Systems

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Authors Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin arXiv ID 2205.01758 Category cs.LG: Machine Learning Cross-listed cs.GR, cs.RO Citations 52 Venue Neural Information Processing Systems Last Checked 3 months ago
Abstract
We present a method for differentiable simulation of soft articulated bodies. Our work enables the integration of differentiable physical dynamics into gradient-based pipelines. We develop a top-down matrix assembly algorithm within Projective Dynamics and derive a generalized dry friction model for soft continuum using a new matrix splitting strategy. We derive a differentiable control framework for soft articulated bodies driven by muscles, joint torques, or pneumatic tubes. The experiments demonstrate that our designs make soft body simulation more stable and realistic compared to other frameworks. Our method accelerates the solution of system identification problems by more than an order of magnitude, and enables efficient gradient-based learning of motion control with soft robots.
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