  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.
