Human bimanual manipulation can perform more complex tasks than a simple combination of two single arms, which is credited to the spatio-temporal coordination between the arms. However, the description of bimanual coordination is still an open topic in robotics. This makes it difficult to give an explainable coordination paradigm, let alone applied to robotics. In this work, we divide the main bimanual tasks in human daily activities into two types: leader-follower and synergistic coordination. Then we propose a relative parameterization method to learn these types of coordination from human demonstration. It represents coordination as Gaussian mixture models from bimanual demonstration to describe the change in the importance of coordination throughout the motions by probability. The learned coordinated representation can be generalized to new task parameters while ensuring spatio-temporal coordination. We demonstrate the method using synthetic motions and human demonstration data and deploy it to a humanoid robot to perform a generalized bimanual coordination motion. We believe that this easy-to-use bimanual learning from demonstration (LfD) method has the potential to be used as a data augmentation plugin for robot large manipulation model training. The corresponding codes are open-sourced in https://github.com/Skylark0924/Rofunc.

翻译：摘要：人类双臂操作能够执行比两个单臂简单组合更复杂的任务，这归功于双臂之间的时空协调。然而，双臂协调的描述在机器人学中仍是一个开放课题。这使得难以给出可解释的协调范式，更不用说将其应用于机器人领域。在本工作中，我们将人类日常活动中的主要双臂任务分为两类：主从式协调与协同式协调。随后提出一种相对参数化方法，通过学习人类示范中的协调模式，将协调关系表示为基于双臂示范的高斯混合模型，通过概率描述协调性在整个运动过程中的重要性变化。所学习的协调表示可泛化至新任务参数，同时确保时空协调性。我们使用合成运动与人类示范数据验证该方法，并将其部署至人形机器人执行通用双臂协调运动。我们相信这种易于使用的双臂学习从示范（LfD）方法，有潜力作为数据增强插件用于机器人大型操作模型训练。相关代码已在https://github.com/Skylark0924/Rofunc开源。