让机器人通过演示学会动态调节柔顺性，更稳更准地抓握物体。
VIDP: Variable Impedance Diffusion Policy for Compliant Robot Manipulation from Diverse Demonstrations
- 从多种示范中学习轨迹分布，推断出物理一致的柔顺控制策略。
- 实测任务成功率显著高于固定柔顺性方法，交互力更小、跟踪误差更低。
- 无需力传感器，适合实际场景中复杂接触任务的机器人操控。
接触密集型操作需要精确跟踪与机械柔顺性，可变阻抗控制能提升任务成功率的鲁棒性，而静态柔顺无法适应变化的接触约束。可变阻抗技能可通过示范学习，避免复杂建模，但柔顺性在无力感知的运动数据中是隐藏变量。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有意的柔顺调整。为此，本文提出可变阻抗扩散策略（VIDP），基于任务参数化方向感知混合模型（TP-DAMM），从多样示范中提取物理一致的轨迹分布，并将其映射为刚度配置，联合预测动作与任务柔顺性，无需力传感器。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，同时相比高刚度控制器降低交互力，相比低刚度基线减少跟踪误差。
原文摘要 · Abstract (English)
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.
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