LUCID：从非结构化人类视频学习与具身无关的意图模型以实现可扩展的灵巧机器人技能获取
LUCID: Learning Embodiment-Agnostic Intent Models from Unstructured Human Videos for Scalable Dexterous Robot Skill Acquisition
AI总结：
提出LUCID两阶段框架，从互联网规模的非结构化人类视频学习任务意图，并在大规模并行仿真中学习机器人控制，实现零样本迁移到不同具身和场景。
摘要：
目前最广泛采用的机器人学习流程通常从机器人演示或结构化人类数据中学习技能，这些数据收集成本高昂且与特定具身绑定。相比之下，非结构化人类视频提供了一种可扩展的替代方案。它们包含跨物体、场景和策略的多样化操作演示，但与机器人动作没有直接联系。我们提出LUCID，一个两阶段框架，从互联网规模数据集的非结构化人类视频中学习任务意图，并在大规模并行仿真中学习机器人控制。意图模型根据当前观测以闭环方式预测短时意图（场景中下一步应该发生什么）。一个具身特定的感觉运动策略将此意图转换为机器人动作。意图接口在控制器之间共享，因此相同的意图模型可应用于不同具身，从我们的主要灵巧手到平行夹爪。我们在五个真实世界操作任务上评估LUCID：搅拌、擦拭和分拣，仅由互联网视频监督，零样本迁移到新场景和物体实例；以及推T和电缆布线，各由1小时自收集智能手机视频监督。项目页面：此 https URL。
英文摘要：
The most widely-adopted robot learning pipelines today learn skills from robot demonstrations or structured human data, which are expensive to collect and tied to specific embodiments. In contrast, unstructured human videos provide a scalable alternative. They contain diverse manipulation demonstrations across objects, scenes, and strategies, but are not directly connected to robot action. We propose LUCID, a two-stage framework that learns task intent from unstructured human videos drawn from internet-scale datasets and learns robot control in massively-parallel simulation. The intent model predicts short-horizon intent (what should happen next in the scene) from the current observation in closed loop. An embodiment-specific sensorimotor policy converts this intent into robot actions. The intent interface is shared across controllers, so the same intent model can be applied to different embodiments, from our primary dexterous hand to a parallel-jaw gripper. We evaluate LUCID on five real-world manipulation tasks: stirring, wiping, and binning supervised by only internet video, with zero-shot transfer to novel scenes and object instances; and push-T and cable routing supervised by 1 hr each of self-collected smartphone video. Project page: https://lucid-robot.github.io/.
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。
