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

BiRP: Learning Robot Generalized Bimanual Coordination using Relative Parameterization Method on Human Demonstration