Reliable autonomous navigation requires adapting the control policy of a mobile robot in response to dynamics changes in different operational conditions. Hand-designed dynamics models may struggle to capture model variations due to a limited set of parameters. Data-driven dynamics learning approaches offer higher model capacity and better generalization but require large amounts of state-labeled data. This paper develops an approach for learning robot dynamics directly from point-cloud observations, removing the need and associated errors of state estimation, while embedding Hamiltonian structure in the dynamics model to improve data efficiency. We design an observation-space loss that relates motion prediction from the dynamics model with motion prediction from point-cloud registration to train a Hamiltonian neural ordinary differential equation. The learned Hamiltonian model enables the design of an energy-shaping model-based tracking controller for rigid-body robots. We demonstrate dynamics learning and tracking control on a real nonholonomic wheeled robot.

翻译：可靠的自主导航需要移动机器人根据不同操作条件下的动力学变化调整控制策略。手设计动力学模型可能因参数有限而难以捕捉模型变化。数据驱动的动力学学习方法具有更高的模型容量和更好的泛化能力，但需要大量状态标注数据。本文提出一种直接从点云观测中学习机器人动力学的方法，消除了状态估计的需求及其相关误差，同时在动力学模型中嵌入哈密顿结构以提高数据效率。我们设计了一种观测空间损失函数，将动力学模型运动预测与点云配准运动预测相关联，以训练哈密顿神经常微分方程。所学的哈密顿模型能够为刚体机器人设计基于能量整形模型的跟踪控制器。我们在真实非完整轮式机器人上演示了动力学学习与跟踪控制。