ContactNets: Learning Discontinuous Contact Dynamics with Smooth, Implicit Representations

• URL: http://arxiv.org/abs/2009.11193v2
• Date: Sun, 1 Nov 2020 06:45:56 GMT
• Title: ContactNets: Learning Discontinuous Contact Dynamics with Smooth, Implicit Representations
• Authors: Samuel Pfrommer and Mathew Halm and Michael Posa
• Abstract summary: Our method learns parameterizations of inter-body signed distance and contact-frame Jacobians. Our method can predict realistic impact, non-penetration, and stiction when trained on 60 seconds of real-world data.
• Score: 4.8986598953553555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Common methods for learning robot dynamics assume motion is continuous, causing unrealistic model predictions for systems undergoing discontinuous impact and stiction behavior. In this work, we resolve this conflict with a smooth, implicit encoding of the structure inherent to contact-induced discontinuities. Our method, ContactNets, learns parameterizations of inter-body signed distance and contact-frame Jacobians, a representation that is compatible with many simulation, control, and planning environments for robotics. We furthermore circumvent the need to differentiate through stiff or non-smooth dynamics with a novel loss function inspired by the principles of complementarity and maximum dissipation. Our method can predict realistic impact, non-penetration, and stiction when trained on 60 seconds of real-world data.

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