Towards understanding and synthesis of contact-rich anthropomorphic motions through interactive cyber-physical human

This article presents perspective on the research challenge of understanding and synthesizing anthropomorphic whole-body contact motions through a platform called "interactive cyber-physical human (iCPH) " for data collection and augmentation. The iCPH platform combines humanoid robots as "physical twins " of human and "digital twins " that simulates humans and robots in cyber-space. Several critical research topics are introduced to address this challenge by leveraging the advanced model-based analysis together with data-driven learning to exploit collected data from the integrated platform of iCPH. Definition of general description is identified as the first topic as a common basis of contact motions compatible to both humans and humanoids. Then, we set continual learning of a feasible contact motion network as the second challenge by benefiting from model-based approach and machine learning bridged by the efficient analytical gradient computation developed by the author and his collaborators. The final target is to establish a high-level symbolic system allowing automatic understanding and generation of contact motions in unexperienced environments. The proposed approaches are still under investigation, and the author expects that this article triggers discussions and further collaborations from different research communities, including robotics, artificial intelligence, neuroscience, and biomechanics.