AI 中文总结
针对多数示范学习方法忽视力与环境相互作用的问题，提出一次性多模态LfD框架。通过多模态概率分割提取力感知运动原语，扩展弹性映射表示并结合外力约束学习轨迹模型，经实验验证该方法具有多模态分割、力感知再现及跨平台通用性。
AI 中文摘要
机器人操作任务通常需要同时对运动和接触力进行推理，但大多数示范学习（LfD）方法仅对空间轨迹建模，忽略了与环境的力相互作用。这种局限性降低了鲁棒性，并可能导致在力约束设置中任务再现不安全或不一致。我们提出了一种新颖的一次性多模态LfD框架，用于分割、编码和再现包含力的示范。首先，引入一种多模态概率分割方法，自适应权衡空间和力模态，自动提取力感知运动原语。其次，扩展弹性映射表示以在技能编码期间纳入外力约束，并制定凸优化程序来学习力一致的轨迹模型。通过考虑示范力轮廓，生成的技能能从单个示范中再现运动和接触特征，同时促进更安全的执行。我们在两种不同的力传感配置下的五个实际操作任务上验证了我们的方法：在配备Robotiq 2f-85夹爪的UR5e上进行腕部力传感，以及在配备Openhand Model O夹爪的Kinova Gen3上进行手指力传感。实验结果证明了强大得的多模态分割、精确的力感知再现和跨平台通用性。
英文摘要
Robotic manipulation tasks often require simultaneous reasoning over motion and contact forces, yet most Learning from Demonstration (LfD) methods model only spatial trajectories and neglect force interactions with the environment. This limitation reduces robustness and can lead to unsafe or inconsistent task reproduction in force-constrained settings. We propose a novel one-shot multimodal LfD framework for the segmentation, encoding, and reproduction of force-inclusive demonstrations. First, we introduce a multimodal probabilistic segmentation method that adaptively weighs spatial and force modalities over time, enabling the automatic extraction of force-aware motion primitives. Second, we extend the elastic maps representation to incorporate external force constraints during skill encoding and formulate a convex optimization procedure for learning force-consistent trajectory models. The resulting skills reproduce both motion and contact characteristics from a single demonstration while promoting safer execution by accounting for demonstrated force profiles. We validate our approach on five real-world manipulation tasks across two distinct force-sensing configurations: wrist force sensing on a UR5e with a Robotiq 2f-85 gripper and finger force sensing on a Kinova Gen3 with an Openhand Model O gripper. Experimental results demonstrate robust multimodal segmentation, accurate force-aware reproduction, and cross-platform generality.
Comments8 pages, 6 figures, 4 tables. Accepted for publication at IROS 2026
