Rather than traditional position control, impedance control is preferred to ensure the safe operation of industrial robots programmed from demonstrations. However, variable stiffness learning studies have focused on task performance rather than safety (or compliance). Thus, this paper proposes a novel stiffness learning method to satisfy both task performance and compliance requirements. The proposed method optimizes the task and compliance objectives (T/C objectives) simultaneously via multi-objective Bayesian optimization. We define the stiffness search space by segmenting a demonstration into task phases, each with constant responsible stiffness. The segmentation is performed by identifying impedance control-aware switching linear dynamics (IC-SLD) from the demonstration. We also utilize the stiffness obtained by proposed IC-SLD as priors for efficient optimization. Experiments on simulated tasks and a real robot demonstrate that IC-SLD-based segmentation and the use of priors improve the optimization efficiency compared to existing baseline methods.

翻译：传统的机器人编程从示教中学习位置控制，但为实现工业机器人安全操作，阻抗控制更受青睐。然而，现有变刚度学习研究主要关注任务性能而非安全性（或柔顺性）。为此，本文提出一种新颖的刚度学习方法，旨在同时满足任务性能与柔顺性要求。该方法通过多目标贝叶斯优化同步优化任务与柔顺性目标（T/C目标）。我们通过将示教轨迹分割为多个任务阶段来定义刚度搜索空间，每个阶段对应恒定且有效的刚度。分割过程通过识别示教数据中的阻抗控制感知切换线性动力学（IC-SLD）实现。同时，将IC-SLD方法获得的刚度作为先验知识，以提升优化效率。在仿真任务和真实机器人上的实验表明，与现有基线方法相比，基于IC-SLD的分割策略及先验知识的引入能显著提升优化效率。