Safe Online Gain Optimization for Variable Impedance Control
This addresses the challenge of adapting robot impedance gains in real-time for safer and more efficient manipulation, though it appears incremental as it builds on existing variable impedance control methods.
The paper tackled the problem of tuning impedance gains for contact-rich manipulation tasks in unstructured environments by proposing Safe OnGO-VIC, which optimizes gains online using force information and safety constraints, resulting in effective and generalizable performance validated on three tasks compared to baselines.
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.