Safe Online Gain Optimization for Variable Impedance Control

TL;DR

This paper introduces Safe OnGO-VIC, a real-time optimization framework for impedance gains in robotic manipulation, ensuring safety and adaptability in unstructured environments.

Contribution

It reformulates impedance control as a control-affine system and develops an online optimization method with safety constraints for variable impedance tuning.

Findings

Effective real-time gain optimization demonstrated on manipulation tasks.

Outperforms baseline and adaptive control methods in experiments.

Ensures safety by embedding collision avoidance in the optimization.

Abstract

Smooth behaviors are preferable for many contact-rich manipulation tasks. Impedance control arises as an effective way to regulate robot movements by mimicking a mass-spring-damping system. Consequently, the robot behavior can be determined by the impedance gains. However, tuning the impedance gains for different tasks is tricky, especially for unstructured environments. Moreover, online adapting the optimal gains to meet the time-varying performance index is even more challenging. In this paper, we present Safe Online Gain Optimization for Variable Impedance Control (Safe OnGO-VIC). 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 variable impedance control. Additionally, we innovatively formulate an optimization problem with online collected force information to obtain the optimal impedance gains in real-time. Safety constraints are also embedded in the proposed framework to avoid unwanted collisions. We experimentally validated the proposed algorithm on three manipulation tasks. Comparison results with a constant gain baseline and an adaptive control method prove that the proposed algorithm is effective and generalizable to different scenarios.