Annan Tang
Biography:
Annan Tang: He is currently a Ph.D. student under the supervision of Prof. Kei Okada and Kunio Kojima at JSK Lab, University of Tokyo. Prior to this, he completed his master’s degree at the University of Tokyo and obtained his bachelor’s degree from Tongji University, China. Additionally, He has one year of research experience as a student researcher at Tencent Robotics-X.
His research interests lie at the intersection of Robotics, Machine Learning, and Control Theory, with a particular focus on utilizing reinforcement learning to empower humanoid robots to autonomously acquire diverse whole-body motion skills.
Abstract
Endowing humanoid robots with natural, versatile, and robust whole-body motion behaviors, similar to those of human beings, remains a persistent challenge due to the complexity of control and intricacies in motion design. While numerous studies based on simplified models and optimal control have shown promising performance in structured motion paradigms, reinforcement learning-based control has recently emerged as a compelling alternative for whole-body control. In this talk, we will first reflect on our previous efforts in model-based control for whole-body humanoid behaviors such as dance and dynamic swing motions. Subsequently, we will introduce our recent Wasserstein adversarial imitation learning framework. Leveraging the power of both reinforcement learning and human motion data, this system enables humanoid robots to demonstrate a wide range of natural whole-body locomotion patterns and execute seamless transitions, including standing, push-recovery, squat walking, human-like straight-leg walking, and dynamic running with a single learned controller. Finally, we endeavor to provide a brief open discussion on integrating both types of control approaches to enhance whole-body control for humanoids.