Xiaolong Wang: Learning Humanoid Robots (2024-10-10)

Talk: Learning Humanoid Robots
Abstract: Having a humanoid robot operating like a human has been a long-standing goal in robotics. The humanoid robot provides a generalized purpose platform to conduct diverse tasks we do in our daily lives. In this talk, we study learning-based approaches for both the mobility and manipulation skills of the humanoid robot, with the goal of generalization to diverse tasks, objects, and scenes. I will discuss how to perform whole-body control in humanoids with rich, diverse, and expressive motions. I will also share some lessons we learned from developing teleoperation systems to operate humanoid robots and collect training data. With the collected data, we aim to build the robot foundation model using a novel RNN architecture with Test-Time Training (TTT).

Speaker: Xiaolong Wang (https://xiaolonw.github.io/)
Bio: Xiaolong Wang is an Assistant Professor in the ECE department at the University of California, San Diego, and a Visiting Professor at NVIDIA Research. He received his Ph.D. in Robotics at Carnegie Mellon University. His postdoctoral training was at the University of California, Berkeley. His research focuses on the intersection between computer vision and robotics. His specific interest lies in learning visual representations from videos and physical robotic interaction data. These comprehensive representations are utilized to facilitate the learning of human-like robot skills, with the goal of generalizing the robot to interact effectively with a wide range of objects and environments in the real physical world. He is the recipient of the J. K. Aggarwal Prize, NSF CAREER Award, Intel Rising Star Faculty Award, and Research Awards from Sony, Amazon, Adobe, and CISCO.