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与断开而具有的混合特性，给高质量控制带来了重大挑战。现有基于模型的方法通常依赖于对多接触模型的良好先验知识，或需要大量离线模型调优工作，从而导致低适应性和鲁棒性。本文提出了一种实时自适应多接触模型预测控制框架，该框架能够实现在线调整混合多接触模型，并持续改善接触密集型任务的控制性能。该框架包含一个自适应模块，该模块持续学习混合模型的残差以最小化先验模型与实际情况之间的差距，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，并将其应用于硬件上解决接触密集型操作任务：机器人使用其末端执行器在桌面上滚动不同未知物体以跟踪给定路径。硬件实验表明，在粗糙先验模型条件下，多接触MPC控制器能够以约20Hz的自适应速率进行在线自适应，并成功操控具有非光滑表面几何特征的未知物体。