Physics-Penalised Regularisation for Learning Dynamics Models with Contact

Abstract

Robotic systems, such as legged robots and manipulators, often handle states which involve ground impact or interaction with objects present in their surroundings; both of which are physically driven by contact. Dynamics model learning tends to focus on continuous motion, yielding poor results when deployed on real systems exposed to non-smooth frictional discontinuities. Inspired by a recent promising direction in machine learning, in this work we present a novel method for learning dynamics models undergoing contact by augmenting data-driven deep models with physics-penalised regularisation. Precisely, this paper conceptually formalises a novel framework for using an impenetrability component in the physics-based loss function directly within the learning objective of neural networks. Our results demonstrate that our method shows superior performance to using normal deep models for learning non-smooth dynamics models of robotic manipulators, strengthening their potential for deployment in contact-rich environments.