稳定以行动：学习双手操作中的协调控制
Stabilize to Act: Learning to Coordinate for Bimanual Manipulation
AI总结：
本文提出角色分配框架 BUDS，通过稳定臂固定物体、执行臂完成任务，并利用再稳定分类器交替协调双臂，在仅 20 个演示下于真实机器人双手任务中取得 76.9% 成功率，显著优于非结构化基线。
摘要：
在现实世界中实现丰富、灵巧的操作，关键在于协调控制双手的能力。然而，尽管双手机器人系统前景广阔，为双臂自主系统构建控制策略仍面临固有困难。其中一个困难是双手动作空间的高维性，这给基于模型的方法和数据驱动的方法都增加了复杂性。为应对这一挑战，我们从人类行为中获得启发，提出了一种新颖的角色分配框架：稳定臂将物体固定到位以简化环境，而执行臂则完成任务。我们用基于稳定的双手灵巧性（BUDS）来实例化该框架，BUDS 使用一个学习到的再稳定分类器，在更新学习到的稳定位置以保持环境不变与利用从演示中学习到的执行策略完成任务之间交替进行。我们在真实机器人上对四个复杂度不同的双手任务进行了评估，例如拉拉链和切蔬菜。仅给定 20 个演示，BUDS 在我们的任务套件中实现了 76.9% 的任务成功率，并且对同一类别内的分布外物体具有 52.7% 的成功率泛化能力。由于这些复杂任务所需的精度，BUDS 比一个非结构化基线方法成功率高 56.0%，该基线方法学习的是 BC 稳定策略。补充材料和视频可在 https://sites.google.com/view/stabilizetoact 找到。
英文摘要：
Key to rich, dexterous manipulation in the real world is the ability to coordinate control across two hands. However, while the promise afforded by bimanual robotic systems is immense, constructing control policies for dual arm autonomous systems brings inherent difficulties. One such difficulty is the high-dimensionality of the bimanual action space, which adds complexity to both model-based and data-driven methods. We counteract this challenge by drawing inspiration from humans to propose a novel role assignment framework: a stabilizing arm holds an object in place to simplify the environment while an acting arm executes the task. We instantiate this framework with BimanUal Dexterity from Stabilization (BUDS), which uses a learned restabilizing classifier to alternate between updating a learned stabilization position to keep the environment unchanged, and accomplishing the task with an acting policy learned from demonstrations. We evaluate BUDS on four bimanual tasks of varying complexities on real-world robots, such as zipping jackets and cutting vegetables. Given only 20 demonstrations, BUDS achieves 76.9% task success across our task suite, and generalizes to out-of-distribution objects within a class with a 52.7% success rate. BUDS is 56.0% more successful than an unstructured baseline that instead learns a BC stabilizing policy due to the precision required of these complex tasks. Supplementary material and videos can be found at https://sites.google.com/view/stabilizetoact .
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。
