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 .

翻译：在现实世界中实现丰富、灵巧操作的关键在于协调双手控制的能力。然而，尽管双臂机器人系统具有巨大潜力，但构建双机械臂自主系统的控制策略却存在固有困难。其中一个困难是双臂动作空间的高维性，这增加了基于模型和数据驱动方法的复杂性。为应对这一挑战，我们受人类启发提出了一种新颖的角色分配框架：稳定臂将物体固定在适当位置以简化环境，而操作臂则执行任务。我们通过稳定化双臂灵巧操作（BUDS）实例化该框架，该框架利用学习得到的再稳定分类器交替更新学习到的稳定位置以保持环境不变，并通过从示范中学习的操作策略完成任务。我们在四种复杂度不同的真实机器人双臂任务上评估BUDS，例如拉拉链夹克和切蔬菜。仅使用20次示范，BUDS在所有任务套件上实现了76.9%的任务成功率，并在同类分布外物体上达到了52.7%的成功率。由于这些复杂任务所需的精度，BUDS比学习行为克隆稳定策略的非结构化基线方法成功率高56.0%。补充材料和视频见https://sites.google.com/view/stabilizetoact。