超越末端执行器：从人类演示学习全臂接触操纵
Beyond the End-Effector: Learning Whole-Arm Contact Manipulation from Human Demonstrations
AI总结：
该研究提出全臂接触学习（WACL）分层框架，从10次裸手人类演示中学习全臂接触操纵，在末端执行器约束下成功率78%，优于基线，且可泛化新约束无需重训。
摘要：
机器人操纵传统上依赖以末端执行器为中心的交互。相比之下，人类经常利用全臂接触来提高效率、鲁棒性和适应性，尤其是在手部被占用或不需要精确抓取时。让机器人具备类似能力仍具挑战性：机器人必须选择手臂上的接触位置、生成有效的非抓取交互，并满足末端执行器的任务特定位姿或安全约束。我们提出全臂接触学习（Whole-Arm Contact Learning, WACL），这是一种分层框架，用三种与机器人无关的表示来描述每个全臂接触任务，并将其传递给准静态表面接触优化器：总结场景的语义关键点、指定手臂在物体上的按压位置和方式的参考接触轨迹，以及视觉语言模型从任务描述生成以保护末端执行器任务的安全感知路径约束。机器人的运动学链仅在该优化器中起作用，该优化器通过联合搜索哪个连杆接触、该连杆上的哪个位置以及实现该接触的关节配置，将这些表示映射到手臂上。这种分解使 WACL 能够从每个任务收集的 10 次裸手人类演示中学习接触轨迹，且无需遥操作；而新的末端执行器约束仅改变优化器的输入，而非任何训练组件。在 7 自由度机械臂上针对四项全臂任务进行的真实世界实验表明，WACL 在末端执行器约束下维持接触的成功率为 78%，而强大的连杆位姿基线仅为 33%，且 WACL 无需重新训练即可泛化到新约束。
英文摘要：
Robot manipulation has traditionally relied on end-effector-centric interactions. In contrast, humans routinely exploit whole-arm contacts to improve efficiency, robustness, and adaptability, especially when the hands are occupied or when precise grasping is unnecessary. Enabling similar capabilities in robots remains challenging: the robot must select where on its arm to make contact, generate effective non-prehensile interactions, and satisfy task-specific pose or safety constraints on the end-effector. We propose Whole-Arm Contact Learning (WACL), a hierarchical framework that describes each whole-arm contact task with three robot-agnostic representations and hands them to a quasi-static surface-contact optimizer: semantic keypoints that summarize the scene, a reference contact trajectory that specifies where and how the arm presses on the object, and safety-aware path constraints that a vision-language model generates from the task description to protect the end-effector's task. The robot's kinematic chain enters only in this optimizer, which grounds these representations onto the arm by jointly searching over which link makes contact, where on that link, and the joint configuration that realizes it. This decomposition lets WACL learn the contact trajectory from 10 bare-hand human demonstrations per task collected without teleoperation, while new end-effector constraints change only the optimizer's input rather than any trained component. Real-world experiments on a 7-DoF arm across four whole-arm tasks show that WACL sustains contact under end-effector constraints where a strong link-pose baseline fails (78% vs. 33% success) and generalizes to new constraints without retraining. Website: this https URL
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