[2205.01758] Differentiable Simulation of Soft Multi-body Systems
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Computer Science > Machine Learning

arXiv:2205.01758 (cs)
[Submitted on 3 May 2022]

Title:Differentiable Simulation of Soft Multi-body Systems

Authors:Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin
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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.
Comments: NeurIPS 2021
Subjects: Machine Learning (cs.LG); Graphics (cs.GR); Robotics (cs.RO)
Cite as: arXiv:2205.01758 [cs.LG]
  (or arXiv:2205.01758v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2205.01758

Submission history

From: Yi-Ling Qiao [view email]
[v1] Tue, 3 May 2022 20:03:22 UTC (8,355 KB)
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