Learning Contact Dynamics using Physically Structured Neural Networks

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Learning Contact Dynamics using Physically Structured Neural Networks

Hochlehnert, A; Terenin, A; Saemundsson, S; Deisenroth, MP; (2021) Learning Contact Dynamics using Physically Structured Neural Networks. In: Banerjee, A and Fukumizu, K, (eds.) Proceedings of AISTATS 2021. (pp. pp. 2152-2160). PMLR Green open access

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Abstract

Learning physically structured representations of dynamical systems that include contact between different objects is an important problem for learning-based approaches in robotics. Black-box neural networks can learn to approximately represent discontinuous dynamics, but they typically require large quantities of data and often suffer from pathological behaviour when forecasting for longer time horizons. In this work, we use connections between deep neural networks and differential equations to design a family of deep network architectures for representing contact dynamics between objects. We show that these networks can learn discontinuous contact events in a data-efficient manner from noisy observations in settings that are traditionally difficult for black-box approaches and recent physics inspired neural networks. Our results indicate that an idealised form of touch feedback—which is heavily relied upon by biological systems—is a key component of making this learning problem tractable. Together with the inductive biases introduced through the network architectures, our techniques enable accurate learning of contact dynamics from observations.

Type:	Proceedings paper
Title:	Learning Contact Dynamics using Physically Structured Neural Networks
Event:	The 24th International Conference on Artificial Intelligence and Statistics
Open access status:	An open access version is available from UCL Discovery
Publisher version:	http://proceedings.mlr.press/v130/hochlehnert21a.h...
Language:	English
Additional information:	This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI:	https://discovery.ucl.ac.uk/id/eprint/10126962