BridgeACT: 通过统一的工具-目标可及性桥接人类示范与机器人动作
BridgeACT: Bridging Human Demonstrations to Robot Actions via Unified Tool-Target Affordances
AI总结：
BridgeACT通过统一的工具-目标可及性表示，直接从人类视频学习机器人操作，无需机器人示范数据，有效提升真实世界执行能力。
摘要：
学习机器人操作从人类视频中学习具有吸引力，因为人类示范的规模和多样性，但将这些示范转换为可执行的机器人行为仍然具有挑战性。先前的工作要么依赖机器人数据进行下游适应，要么学习可及性表示，这些表示停留在感知层面，无法直接支持现实世界执行。我们提出了BridgeACT，一种以可及性驱动的框架，直接从人类视频中学习机器人操作，而无需任何机器人示范数据。我们的核心思想是将可及性建模为一种与身体无关的中间表示，从而桥接人类示范和机器人动作。BridgeACT将操作分解为两个互补的问题：在哪里抓取和如何移动。为此，BridgeACT首先在当前场景中定位与任务相关的可及性区域，然后从人类示范中预测任务条件的3D运动可及性。所得到的可及性通过抓取模块和轻量级闭环运动控制器映射到机器人动作，使可以直接部署在真实机器人上。此外，我们还将复杂操作任务表示为可及性操作的组合，这允许对多样化任务和物体间交互进行统一处理。在真实世界操作任务上的实验表明，BridgeACT优于先前的基线，并且能够泛化到未见过的物体、场景和视角。
英文摘要：
Learning robot manipulation from human videos is appealing due to the scale and diversity of human demonstrations, but transferring such demonstrations to executable robot behavior remains challenging. Prior work either relies on robot data for downstream adaptation or learns affordance representations that remain at the perception level and do not directly support real-world execution. We present BridgeACT, an affordance-driven framework that learns robotic manipulation directly from human videos without requiring any robot demonstration data. Our key idea is to model affordance as an embodiment-agnostic intermediate representation that bridges human demonstrations and robot actions. BridgeACT decomposes manipulation into two complementary problems: where to grasp and how to move. To this end, BridgeACT first grounds task-relevant affordance regions in the current scene, and then predicts task-conditioned 3D motion affordances from human demonstrations. The resulting affordances are mapped to robot actions through a grasping module and a lightweight closed-loop motion controller, enabling direct deployment on real robots. In addition, we represent complex manipulation tasks as compositions of affordance operations, which allows a unified treatment of diverse tasks and object-to-object interactions. Experiments on real-world manipulation tasks show that BridgeACT outperforms prior baselines and generalizes to unseen objects, scenes, and viewpoints.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
