AI 中文总结
该研究针对接触丰富的机器人操作问题，提出基于模仿学习的可变阻抗控制框架VIDP，利用TP-DAMM从多样本演示中提取物理一致的轨迹分布，无需力传感器即可联合预测位姿与柔顺性，在真实实验中任务成功率及力、跟踪误差表现均优于基线方法。
AI 中文摘要
接触丰富的操作需要精确的轨迹跟踪和机械柔顺性，其中可变阻抗控制可提高任务成功的鲁棒性，而静态柔顺性无法适应变化的接触约束。可变阻抗技能可从演示中学习，避免复杂建模，但柔顺性是力无关运动学数据中的隐藏变量。现有方法通过轨迹变化推断柔顺性，但这些变化可能反映几何适应，而非在空间布局变化时的有意柔顺性。因此，本文提出Variable Impedance Diffusion Policy（VIDP），这是一种基于模仿学习的可变阻抗控制框架，利用任务参数化方向感知混合模型（TP-DAMM）从多样本演示中提取物理一致的轨迹分布。通过将分布映射到刚度曲线，VIDP无需力传感器即可联合预测位姿动作和任务柔顺性。真实世界实验表明，VIDP在任务成功率上显著优于固定阻抗基线，相比高刚度控制器降低了交互力，相比低刚度基线减少了跟踪误差。
英文摘要
Contact-rich manipulation requires precise tracking and mechanical compliance, where variable impedance control can improve robustness in task success, whereas static compliance cannot adapt to varying contact constraints. Variable impedance skills can be learned from demonstrations, avoiding complex modeling, but compliance is a hidden variable in force-agnostic kinematic data. While existing methods infer compliance from trajectory variations, these variations may reflect geometric adaptation and not intentional compliance when subject to changing spatial layouts. Therefore, this letter introduces Variable Impedance Diffusion Policy (VIDP), an imitation learning-based variable impedance control framework leveraging a Task-Parameterized Directionality-Aware Mixture Model (TP-DAMM) to extract physically consistent trajectory distributions from diverse demonstrations. By mapping distributions to stiffness profiles, VIDP jointly predicts pose actions and task compliance without force sensors. Real-world experiments show that VIDP significantly outperforms fixed-impedance baselines in task success rate while reducing interaction forces with respect to high stiffness controllers and tracking errors with respect to low stiffness baselines.
Comments8 pages, 5 figures
