Composite Learning Adaptive Interaction Control for High-DoF Collaborative Robots*

Abstract

Variable impedance control is essential for improving safety and naturalness during physical human-robot interaction. However, the robot target impedance is usually difficult to be realized for existing variable impedance control methods. This paper proposes a composite learning adaptive interaction control strategy for robots to achieve target impedance under parametric uncertainty. A multi-mode adaptive control scheme is defined based on weighting functions to ensure smooth mode transitions. A composite learning technique is applied for exact robot modeling online such that target impedance can be achieved under interval excitation that is much weaker than persistent excitation. The proposed method can achieve variable stiffness and damping with guaranteed stable and safe interaction. Experiments on a collaborative robot with 7 degrees of freedom named Franka Emika Panda have validated the effectiveness and superiority of the proposed method.