Controlling contacts is truly challenging, and this has been a major hurdle to deploying industrial robots into unstructured/human-centric environments. More specifically, the main challenges are: (i) how to ensure stability at all times; (ii) how to satisfy task-specific performance specifications; (iii) how to achieve (i) and (ii) under environment uncertainty, robot parameters uncertainty, sensor and actuator time delays, external perturbations, etc. Here, we propose a new approach -- Convex Controller Synthesis (CCS) -- to tackle the above challenges based on robust control theory and convex optimization. In two physical interaction tasks -- robot hand guiding and sliding on surfaces with different and unknown stiffnesses -- we show that CCS controllers outperform their classical counterparts in an essential way.

中文翻译：

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机器人接触的凸面控制器综合

控制接触确实具有挑战性，这一直是将工业机器人部署到非结构化/以人为中心的环境中的主要障碍。更具体地说，主要挑战是：(i) 如何始终确保稳定；(ii) 如何满足特定任务的性能规范；(iii) 如何在环境不确定性、机器人参数不确定性、传感器和执行器时间延迟、外部扰动等情况下实现 (i) 和 (ii)。在这里，我们提出了一种新方法——凸控制器合成（CCS）——以基于鲁棒控制理论和凸优化解决上述挑战。在两个物理交互任务中——机器人手引导和在具有不同和未知刚度的表面上滑动——我们表明 CCS 控制器在本质上优于经典的对应物。