Self Model for Embodied Intelligence: Modeling and Control of Full-Body Human Musculoskeletal System

Associate Professor Yanan Sui
Tsinghua University, China

Modeling and control of the human musculoskeletal system is important for understanding human motor functions, developing embodied intelligence, and optimizing human-robot interaction systems. However, current models are restricted to a limited range of body parts and often with a reduced number of muscles. There is also a lack of algorithms capable of controlling over 600 muscles to generate reasonable human movements. To fill this gap, we build a musculoskeletal model with 90 body segments, 206 joints, and 700 muscle-tendon units, allowing simulation of full-body dynamics and interaction with various devices. We develop a new algorithm using low-dimensional representation and hierarchical deep reinforcement learning to achieve state-of-the-art full-body control. We validate the effectiveness of our model and algorithm in simulations with real human locomotion data. This work promotes a deeper understanding of human motion control and better design of interactive robots.

Biosketch

Yanan Sui associate professor at Tsinghua University, is committed to the research of human neuro-musculo-skeletal modeling and control, with applications in embodied intelligence and brain-machine interaction. He received bachelor's degree from Tsinghua University, PhD from Caltech, and engaged in postdoc work at Caltech and Stanford University. His work on safe optimization was written into textbooks of Stanford and other universities. He got the Best Conference Paper Award and Best Paper Award on Human-Robot Interaction at the 2020 International Conference on Robotics and Automation. His work was successfully applied to the clinical treatment of neural injuries in China and the United States. He has served as area chair of artificial intelligence conferences including AAAI, AISTATS, ICLR, ICML, NeurIPS. For his contribution in the interdisciplinary field of artificial intelligence and neural engineering, he was selected as the MIT Technology Review's Innovators Under 35 in China.