The hybrid nature of multi-contact robotic systems, due to making and breaking contact with the environment, creates significant challenges for high-quality control. Existing model-based methods typically rely on either good prior knowledge of the multi-contact model or require significant offline model tuning effort, thus resulting in low adaptability and robustness. In this paper, we propose a real-time adaptive multi-contact model predictive control framework, which enables online adaption of the hybrid multi-contact model and continuous improvement of the control performance for contact-rich tasks. This framework includes an adaption module, which continuously learns a residual of the hybrid model to minimize the gap between the prior model and reality, and a real-time multi-contact MPC controller. We demonstrated the effectiveness of the framework in synthetic examples, and applied it on hardware to solve contact-rich manipulation tasks, where a robot uses its end-effector to roll different unknown objects on a table to track given paths. The hardware experiments show that with a rough prior model, the multi-contact MPC controller adapts itself on-the-fly with an adaption rate around 20 Hz and successfully manipulates previously unknown objects with non-smooth surface geometries.

翻译：多接触机器人系统因与环境接触的建立与断开而具有混合特性，这给高质量控制带来了显著挑战。现有基于模型的方法通常依赖于良好的多接触模型先验知识，或需要大量的离线模型调优工作，导致其适应性和鲁棒性较低。本文提出了一种实时自适应多接触模型预测控制框架，能够实现混合多接触模型的在线自适应，并持续提升接触密集型任务的控制性能。该框架包含一个自适应模块（用于持续学习混合模型的残差以缩小先验模型与现实之间的差距）以及一个实时多接触MPC控制器。我们通过合成算例验证了该框架的有效性，并将其应用于硬件以解决接触密集型操作任务：机器人使用末端执行器在桌面上滚动不同未知物体以跟踪指定路径。硬件实验表明，在仅有粗略先验模型的情况下，该多接触MPC控制器能够以约20Hz的自适应速率在线调整自身，并成功操作具有非光滑表面几何特征的未知物体。