AnyBimanual：将单臂策略迁移至通用双臂操作
AnyBimanual: Transferring Unimanual Policy for General Bimanual Manipulation
AI总结：
提出AnyBimanual，通过技能管理器与视觉对齐器将预训练单臂策略迁移至双臂操作，以少量演示实现通用双臂操作，在模拟和真实任务中显著提升成功率。
摘要：
执行通用的语言条件化双臂操作任务对于从家庭服务到工业装配的许多应用都至关重要。然而，由于高维动作空间，收集双臂操作数据的成本高昂，这对传统方法处理通用双臂操作任务构成了挑战。相比之下，单臂策略最近因模型参数和训练数据的规模化而展现出跨广泛任务的显著泛化能力，这可以为双臂系统提供可共享的操作知识。为此，我们提出了一种即插即用的方法，名为AnyBimanual，该方法通过少量双臂演示将预训练的单臂策略迁移至通用双臂操作策略。具体而言，我们首先引入一个技能管理器，动态调度从预训练单臂策略中发现用于双臂操作任务的技能表示，该管理器将技能原语与面向任务的补偿线性组合，以表示双臂操作指令。为缓解单臂与双臂系统之间的观测差异，我们提出一个视觉对齐器，为工作空间的视觉嵌入生成软掩码，旨在将每个手臂的单臂策略模型的视觉输入与预训练阶段的视觉输入对齐。AnyBimanual在RLBench2的12个模拟任务上表现出优越性，成功率较先前方法显著提升12.67%。在9个真实世界任务上的实验进一步验证了其实用性，平均成功率达到84.62%。
英文摘要：
Performing general language-conditioned bimanual manipulation tasks is of great importance for many applications ranging from household service to industrial assembly. However, collecting bimanual manipulation data is expensive due to the high-dimensional action space, which poses challenges for conventional methods to handle general bimanual manipulation tasks. In contrast, unimanual policy has recently demonstrated impressive generalizability across a wide range of tasks because of scaled model parameters and training data, which can provide sharable manipulation knowledge for bimanual systems. To this end, we propose a plug-and-play method named AnyBimanual, which transfers pre-trained unimanual policy to general bimanual manipulation policy with few bimanual demonstrations. Specifically, we first introduce a skill manager to dynamically schedule the skill representations discovered from pre-trained unimanual policy for bimanual manipulation tasks, which linearly combines skill primitives with task-oriented compensation to represent the bimanual manipulation instruction. To mitigate the observation discrepancy between unimanual and bimanual systems, we present a visual aligner to generate soft masks for visual embedding of the workspace, which aims to align visual input of unimanual policy model for each arm with those during pretraining stage. AnyBimanual shows superiority on 12 simulated tasks from RLBench2 with a sizable 12.67% improvement in success rate over previous methods. Experiments on 9 real-world tasks further verify its practicality with an average success rate of 84.62%.
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。
