仅凭一次演示，让机器人学会兼顾动作与受力的智能操作。
One-Shot Multimodal Learning from Demonstration with Force-Constrained Elastic Maps
- 通过自适应融合空间轨迹与受力信号，自动提取力感知动作单元。
- 在单次演示下实现运动与接触力的精准复现，提升操作安全性。
- 适用于多种机械臂与传感器配置，实测表现稳定可靠。
机器人操作任务常需同时处理运动与接触力，但多数示教学习（LfD）方法仅建模空间轨迹，忽略与环境的力交互，导致鲁棒性差，在受力约束场景中易出现不安全或不一致的任务执行。本文提出一种新型一次性多模态示教学习框架，用于力信息的分割、编码与复现。首先，设计一种多模态概率分割方法，动态加权时空与力信号，自动提取力感知的动作基元。其次，将弹性映射扩展至包含外部力约束的技能编码，并构建凸优化求解过程，学习符合力特征的轨迹模型。所生成技能能从单次演示中复现运动与接触特性，且通过考虑示范中的力分布，促进更安全的执行。我们在两个不同力传感配置下的五项真实世界操作任务中验证该方法：基于UR5e机械臂与Robotiq 2f-85夹持器的腕部力传感，以及基于Kinova Gen3机械臂与Openhand Model O夹持器的手指力传感。实验结果表明，该方法具备稳健的多模态分割能力、准确的力感知复现性能，且具有跨平台通用性。
原文摘要 · Abstract (English)
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.
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