Physics-penalised Regularisation for Learning Dynamics Models with Contact
Proceedings of the 3rd Conference on Learning for Dynamics and Control, PMLR 144:611-622, 2021.
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.