Stabilize to Act: Learning to Coordinate for Bimanual Manipulation
Stabilize to Act: Learning to Coordinate for Bimanual Manipulation
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DOI：
10.48550/arxiv.2309.01087
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发表时间：
2023-09
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影响因子：
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通讯作者：
J. Grannen;Yilin Wu;Brandon Vu;Dorsa Sadigh
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作者：
J. Grannen;Yilin Wu;Brandon Vu;Dorsa Sadigh
在现实世界中，丰富、灵巧的操作的关键是两只手协调控制的能力。然而，虽然双手机器人系统的前景是巨大的，但构建双臂自主系统的控制策略也带来了固有的困难。其中一个困难是手工操作空间的高维性，这增加了基于模型和数据驱动方法的复杂性。为了应对这一挑战，我们从人类身上汲取灵感，提出了一种新的角色分配框架：稳定手臂将物体固定在适当的位置，以简化环境，而行动手臂执行任务。我们用双手灵巧稳定化（BUDS）实例化了这个框架，它使用一个学习到的再稳定分类器来交替更新学习到的稳定位置以保持环境不变，并使用从演示中学习到的行为策略来完成任务。我们在现实世界的机器人上对四种不同复杂程度的人工任务进行了评估，比如拉夹克拉链和切蔬菜。仅给出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，并以52.7%的成功率推广到类中的分布外对象。由于这些复杂任务的精度要求，与学习BC稳定策略的非结构化基线相比，BUDS的成功率高56.0%。补充材料和视频可在https://sites.google.com/view/stabilizetoact上找到。
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 .