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：可靠自主导航需要根据不同操作条件下的动力学变化调整移动机器人的控制策略。手工设计的动力学模型可能因参数集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模型容量和更强泛化能力，但需要大量状态标记数据。本文提出一种直接从点云观测学习机器人动力学的方法，消除了状态估计及其相关误差的需求，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，将动力学模型的运动预测与点云配准的运动预测关联起来，用于训练哈密顿神经常微分方程。通过学习得到的哈密顿模型，可设计基于能量整形的模型跟踪控制器用于刚体机器人。我们在真实非完整轮式机器人上验证了动力学学习和跟踪控制的性能。