通过动作分块与臂间协调的 Transformer 学习双臂操作
Learning Bimanual Manipulation via Action Chunking and Inter-Arm Coordination with Transformers
AI总结：
针对高自由度双臂难以同步协调的问题，提出含 IACE 的差异化 Transformer 模仿学习架构，通过动作分块与臂间时间对齐提升双臂操作成功率。
摘要：
能够在人类居住环境中自主运行的机器人，必须具备灵活处理各种任务的能力。其中一个关键要素是协调的双臂运动，它能实现仅凭单手难以完成的功能。近年来，已有一些聚焦双臂运动可能性的基于学习的模型被提出。然而，机器人的高自由度使其控制推理极具挑战，并且左右机械臂需要根据情境调整动作，这使得更灵巧的任务难以实现。为解决这一问题，我们关注双臂之间的协调与效率，尤其是同步动作。因此，我们提出了一种用于预测协作动作的新型模仿学习架构。我们对两条手臂采用差异化架构，并加入一个中间编码器层——Inter-Arm Coordinated transformer Encoder（IACE，臂间协调 Transformer 编码器），以促进同步与时间对齐，确保动作平滑且协调。为验证所提架构的有效性，我们执行了具有代表性的双臂任务。实验结果表明，与对比方法相比，我们的模型展现出较高成功率，并表明该架构适合用于双臂操作的策略学习。
英文摘要：
Robots that can operate autonomously in a human living environment are necessary to have the ability to handle various tasks flexibly. One crucial element is coordinated bimanual movements that enable functions that are difficult to perform with one hand alone. In recent years, learning-based models that focus on the possibilities of bimanual movements have been proposed. However, the high degree of freedom of the robot makes it challenging to reason about control, and the left and right robot arms need to adjust their actions depending on the situation, making it difficult to realize more dexterous tasks. To address the issue, we focus on coordination and efficiency between both arms, particularly for synchronized actions. Therefore, we propose a novel imitation learning architecture that predicts cooperative actions. We differentiate the architecture for both arms and add an intermediate encoder layer, Inter-Arm Coordinated transformer Encoder (IACE), that facilitates synchronization and temporal alignment to ensure smooth and coordinated actions. To verify the effectiveness of our architectures, we perform distinctive bimanual tasks. The experimental results showed that our model demonstrated a high success rate for comparison and suggested a suitable architecture for the policy learning of bimanual manipulation.
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。
