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.
翻译:包括不同天体之间接触的动态系统的物理结构化表现是机器人中基于学习的方法的一个重要问题。黑盒神经网络可以学会大约代表不连续的动态,但它们通常需要大量的数据,在预测较长的时间跨度时往往会受到病理行为的影响。在这项工作中,我们利用深神经网络和差异方程式之间的联系来设计一个深网络结构组成的大家庭,以代表不同天体之间的接触动态。我们表明,这些网络可以通过在传统上对黑盒方式和最近物理学启发神经网络十分困难的环境下进行的吵闹观测,以数据效率高的方式,从中学习不连续的接触事件。我们的结果表明,一种理想的触摸反馈形式 -- -- 生物系统非常依赖这种形式 -- -- 是使这一学习问题可以感动的一个关键组成部分。与通过网络结构引入的诱导偏见一道,我们的技术能够从观测中准确学习接触动态。