可变障碍控制安全在线收益优化 (Safe Online Gain Optimization for Variable Impedance Control)

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.

翻译：光滑行为更适合许多接触丰富的操作任务。障碍控制是模拟大规模冲刺系统来监管机器人运动的有效方法。因此, 机器人行为可以通过阻力增益来决定。但是, 调整不同任务的阻力增益是棘手的, 特别是对于无结构的环境。此外, 在线调整最佳增益以适应时间变化性性性能指数更具有挑战性。在本文中, 我们介绍了可变障碍控制的安全在线增益优化。通过将阻力控制动态调整为控制室系统( 安全 OnGO- VIC ) 。通过将阻力增益作为控制室系统, 我们提供了一种新视角来理解可变阻力控制。此外, 我们创新地利用在线收集的武力信息来形成优化问题, 以获得实时的最佳阻力增益。安全限制也嵌入了拟议框架中以避免意外碰撞。我们实验性地验证了三种操纵任务的拟议算法。与固定增益基线和适应性控制方法的对比结果证明, 拟议的算法是有效和可概括到不同情景的。