RT-Affordance：可供性作为机器人操作的多功能中间表示
RT-Affordance: Affordances are Versatile Intermediate Representations for Robot Manipulation
AI总结：
RT-Affordance提出以可供性为中间表示，分层生成可供性计划并条件化策略，利用互联网数据和领域内图像，在多样新任务上性能提升超50%。
摘要：
我们探讨了中间策略表示如何通过提供操作任务的执行指导来促进泛化。已有的表示方法，如语言、目标图像和轨迹草图，已被证明是有帮助的，但这些表示要么提供的上下文不足，要么提供的上下文过度具体化，导致策略不够稳健。我们提出将策略条件建立在可供性（affordances）上，可供性捕捉了任务关键阶段机器人的位姿。可供性提供了表达力强且轻量级的抽象，易于用户指定，并通过从大型互联网数据集迁移知识促进高效学习。我们的方法RT-Affordance是一个分层模型，首先根据任务语言提出可供性计划，然后在该可供性计划上条件化策略以执行操作。我们的模型能够灵活地桥接异构监督来源，包括大型网络数据集和机器人轨迹。我们还在易于收集的领域内可供性图像上训练模型，从而无需收集额外昂贵的机器人轨迹即可学习新任务。我们在多样化的新任务上展示了RT-Affordance的性能比现有方法高出超过50%，并实证证明了可供性对新场景具有稳健性。视频可在 https://snasiriany.me/rt-affordance 获取。
英文摘要：
We explore how intermediate policy representations can facilitate generalization by providing guidance on how to perform manipulation tasks. Existing representations such as language, goal images, and trajectory sketches have been shown to be helpful, but these representations either do not provide enough context or provide over-specified context that yields less robust policies. We propose conditioning policies on affordances, which capture the pose of the robot at key stages of the task. Affordances offer expressive yet lightweight abstractions, are easy for users to specify, and facilitate efficient learning by transferring knowledge from large internet datasets. Our method, RT-Affordance, is a hierarchical model that first proposes an affordance plan given the task language, and then conditions the policy on this affordance plan to perform manipulation. Our model can flexibly bridge heterogeneous sources of supervision including large web datasets and robot trajectories. We additionally train our model on cheap-to-collect in-domain affordance images, allowing us to learn new tasks without collecting any additional costly robot trajectories. We show on a diverse set of novel tasks how RT-Affordance exceeds the performance of existing methods by over 50%, and we empirically demonstrate that affordances are robust to novel settings. Videos available at https://snasiriany.me/rt-affordance
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。
