Adaptive Contact-Implicit Model Predictive Control with Online Residual Learning
Adaptive Contact-Implicit Model Predictive Control with Online Residual Learning
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DOI：
10.48550/arxiv.2310.09893
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发表时间：
2023-10
期刊：
影响因子：
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通讯作者：
Wei-Cheng Huang;Alp Aydinoglu;Wanxin Jin;Michael Posa
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文献类型：
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作者：
Wei-Cheng Huang;Alp Aydinoglu;Wanxin Jin;Michael Posa
由于与环境接触和断开接触，多接触机器人系统的混合性质为高质量控制带来了重大挑战。现有的基于模型的方法通常依赖于多接触模型的良好先验知识，或者需要大量的离线模型调整工作，从而导致低适应性和鲁棒性。在本文中，我们提出了一个实时自适应多接触模型预测控制框架，它使在线自适应的混合多接触模型和接触丰富的任务的控制性能的不断改善。该框架包括一个自适应模块，它不断地学习混合模型的残差，以最小化先验模型和现实之间的差距，和一个实时多接触MPC控制器。我们在合成示例中展示了该框架的有效性，并将其应用于硬件来解决接触丰富的操作任务，其中机器人使用其末端执行器在桌子上滚动不同的未知物体以跟踪给定的路径。硬件实验表明，在粗略的先验模型下，多接触MPC控制器以约20 Hz的自适应速率进行动态自适应，并成功操纵具有非光滑表面几何形状的先前未知物体。
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.