Human-Informed Robot Agility: Understanding Human Pushing Interactions for Skill Transfer to Humanoids

This research focuses on the analysis of human dynamic interactions and their applicability to robot systems. Studying human mechanics and control strategies in these interactions provides valuable insights for developing control approaches for dynamic interactions in robots. This work captures and analyzes wall push-offs as a specific interaction, and develops a skill model capturing push-off behaviors adaptable to different robot embodiments. Pushing skills are modeled as position trajectories relative to the center of mass, implicitly encoding interaction forces. We show that this approach can be scaled and adapted through optimization, and present results for transferring wall push-offs to two different robot platforms. Videos and additional data are available on the project website (https://sites.google.com/andrew.cmu.edu/human-robot-agility).