Imitation Learning With Time-Varying Synergy for Compact Representation of Spatiotemporal Structures

Imitation learning is a promising approach for robots to learn complex motor skills. Recent techniques allow robots to learn long-term movements comprising multiple sub-behaviors. However, learning the temporal structures of movements from a demonstration is challenging, particularly when sub-behaviors overlap and are not labeled in advance. This study applied time-varying synergies, which are representations of spatial and temporal structures in human behavior in neuroscience, to imitation learning. The proposed method extracts time-varying synergies from human demonstrations, with neural networks that learn their activation patterns. Because time-varying synergies can decompose demonstrations into linear combinations of primitives while allowing overlapping, neural networks can learn demonstrations efficiently. This would make the model compact and improve its generalization ability. The proposed method was evaluated with the task of cursive letter writing requiring overlapping sub-behaviors. Consequently, the proposed method allows a neural network to generate new movements with a higher success rate and fewer parameters than those without the proposed method. Moreover, the neural network worked robustly against control deviations and disturbances in an actual robot.