使用物理结构神经网络学习接触动力学
Learning Contact Dynamics using Physically Structured Neural Networks
摘要：
学习包含不同物体间接触的动力系统的物理结构化表示是机器人学中基于学习方法的一个重要问题。黑盒神经网络可以学习近似表示不连续的动态，但它们通常需要大量数据，并且在预测较长时间范围时经常会出现病态行为。在这项工作中，我们使用深度神经网络和微分方程之间的连接来设计一系列深度网络架构，用于表示对象之间的接触动态。我们表明，这些网络可以学习不连续的接触事件在一个数据有效的方式从噪声观测的设置，传统上是很难的黑箱方法和最近的物理启发神经网络。我们的研究结果表明，一种理想化的触摸反馈形式--生物系统严重依赖这种反馈--是使这种学习问题变得易于处理的关键组成部分。再加上通过网络架构引入的归纳偏差，我们的技术能够从观察中准确地学习接触动态。
英文摘要：
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.
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