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%.

翻译：执行通用的语言条件双臂操作任务对于从家庭服务到工业装配的众多应用至关重要。然而，由于高维动作空间，收集双臂操作数据成本高昂，这给传统方法处理通用双臂操作任务带来了挑战。相比之下，单臂策略近期因其大规模模型参数和训练数据，在广泛任务中展现出令人印象深刻的泛化能力，可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方法AnyBimanual，该方法利用少量双臂演示，将预训练的单臂策略迁移为通用双臂操作策略。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的技能表示以用于双臂操作任务，该方法将技能基元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间的观测差异，我们提出了一个视觉对齐器，为工作空间的视觉嵌入生成软掩码，旨在将单臂策略模型中每个手臂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，成功率较先前方法大幅提升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%。