Synchronized Human-Humanoid Motion Imitation

We present a tele-operation control framework that (i) enhances the upper motion synchrony between a user and a robot using the minimum-jerk model coupled with a recursive least-square filter, and (ii) synchronizes the walking pace by predicting user's stepping frequency using motion capture data and a deep learning model. By integrating (i) and (ii) in a task-space whole-body controller, we achieve full-body synchronization. We assess our humanoid-to-human whole-body synchronized motion model on the HRP-4 humanoid robot in forward, lateral and backward walks with concurrent upper limbs motions experiments.

Automated summary

We propose a tele-operation control framework that improves the upper motion synchrony between a user and a robot using the minimum-jerk model and a recursive least-square filter. We also synchronize the walking pace by predicting the user's stepping frequency using motion capture data and a deep learning model. By integrating these two methods in a task-space whole-body controller, we achieve full-body synchronization. We evaluate our model on the HRP-4 humanoid robot in experiments involving forward, lateral, and backward walks with concurrent upper limb motions.