Convex Controller Synthesis for Robot Contact

TL;DR

This paper introduces Convex Controller Synthesis (CCS), a robust control method using convex optimization, to improve stability and performance of robots during physical contact tasks under various uncertainties.

Contribution

The paper presents a novel CCS approach that leverages robust control theory and convex optimization to enhance robot contact control in uncertain environments.

Findings

CCS controllers outperform classical methods in physical interaction tasks.

CCS maintains stability under environment and parameter uncertainties.

Experimental results validate the effectiveness of CCS in guiding and surface sliding tasks.

Abstract

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.