Safe Online Gain Optimization for Cartesian Space Variable Impedance Control

摘要

Smooth behaviors are preferable for many contact-rich manipulation tasks. Impedance control provides an effective way to regulate robot movements by mimicking motions of a mass-spring-damping system. Consequently, the robot behavior can be determined by the impedance gains. However, tuning of the impedance gains for different tasks is not straightforward, especially for unstructured environments. Moreover, online adaption of the optimal gains to deal with the time-varying performance index is even more challenging. In this paper, we present Safe Online Gain Optimization for Cartesian space Variable Impedance Control (Safe OnGO-VIC) to overcome these challenges. By reformulating the dynamics of impedance control as a control-affine system, in which the impedance gains are the inputs, we provide a novel perspective to understand the relation between impedance gains and the robot behaviors. Furthermore, we innovatively formulate an optimization problem that utilizes force measurement collected online to obtain the optimal impedance gains in real-time. Safety constraints are also embedded in the proposed framework. We experimentally validated the proposed algorithm on three contact-rich manipulation tasks. Comparison results with a constant gain baseline and an adaptive control baseline prove that the proposed algorithm is effective and generalizable to different scenarios. Experiment videos are available at https://msc.berkeley.edu/research/safe-ongo-vic.html.