Accurate dynamics models are critical for the design of predictive controller for autonomous mobile robots. Physics-based models are often too simple to capture relevant real-world effects, while data-driven models are data-intensive and slow to train. We introduce an approach for fast adaptation of neural robot dynamic models that combines offline training with efficient online updates. Our approach learns an incremental neural dynamics model offline and performs low-rank second-order parameter adaptation online, enabling rapid updates without full retraining. We demonstrate the approach on a real quadrotor robot, achieving robust predictive tracking control in novel operational conditions.

翻译：精确的动力学模型对于自主移动机器人预测控制器的设计至关重要。基于物理的模型往往过于简化而无法捕捉实际场景中的关键效应，而数据驱动的模型则存在数据需求量大、训练速度慢的问题。本文提出一种结合离线训练与高效在线更新的神经机器人动力学模型快速适应方法。该方法通过离线学习增量式神经动力学模型，并在在线阶段实施低秩二阶参数自适应，无需完整重训练即可实现快速更新。我们在真实四旋翼机器人上验证了该方法，在新型运行条件下实现了鲁棒的预测跟踪控制。