DexKnot: 通用的视觉-运动政策学习用于灵巧的袋结操作
DexKnot: Generalizable Visuomotor Policy Learning for Dexterous Bag-Knotting Manipulation
AI总结：
DexKnot通过结合关键点暗示与扩散策略，实现对灵巧袋结操作的通用政策学习，有效提升机器人在不同袋子实例和变形下的编织性能。
摘要：
编织塑料袋是日常生活中常见的任务，但对机器人来说却具有挑战性，因为袋子具有无限的自由度和复杂的物理动态。现有方法在泛化到未见过的袋子实例或变形时往往表现不佳。为了解决这一问题，我们提出了DexKnot，一种结合关键点暗示与扩散策略的框架，用于学习通用的袋结政策。我们的方法通过收集现实世界的手动变形中的关键点对应数据，学习一种与形状无关的袋子表示。对于未见过的袋子配置，可以通过匹配表示到参考来识别关键点。这些关键点随后被提供给扩散变压器，根据少量的人类演示生成机器人动作。DexKnot通过将观察空间的维度减少到稀疏的关键点集合，实现了有效的政策泛化。实验表明，DexKnot在各种之前未见过的实例和变形上实现了可靠且一致的结编织性能。
英文摘要：
Knotting plastic bags is a common task in daily life, yet it is challenging for robots due to the bags' infinite degrees of freedom and complex physical dynamics. Existing methods often struggle in generalization to unseen bag instances or deformations. To address this, we present DexKnot, a framework that combines keypoint affordance with diffusion policy to learn a generalizable bag-knotting policy. Our approach learns a shape-agnostic representation of bags from keypoint correspondence data collected through real-world manual deformation. For an unseen bag configuration, the keypoints can be identified by matching the representation to a reference. These keypoints are then provided to a diffusion transformer, which generates robot action based on a small number of human demonstrations. DexKnot enables effective policy generalization by reducing the dimensionality of observation space into a sparse set of keypoints. Experiments show that DexKnot achieves reliable and consistent knotting performance across a variety of previously unseen instances and deformations.
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