Kavli Affiliate: Xiang Zhang
| First 5 Authors: Changhao Wang, Zhian Kuang, Xiang Zhang, Masayoshi Tomizuka,
| Summary:
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.
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