CREATE ADVENTOR Presents: Dr. Chao Liu

Structured Intelligence for Robot Manipulation

Abstract

Contact-rich manipulation requires compliant bodies for safe, adaptive interaction, yet remains challenging because robots must reason about real-world complexity and uncertainty in real time. Learning-based approaches have shown impressive effectiveness, and scaling laws suggest the power of more data, yet their internal mechanisms remain poorly understood. This talk presents a perspective on building structured intelligence for physical interaction. We begin with compliant and proprioceptive actuation for robotic manipulation, showing how soft-rigid hybrid structures can retain the safety and adaptability of soft materials while gaining the sensing, observability, and control precision needed for dexterous manipulation. We then examine structured tactile sensing and visual-tactile learning as ways to recover useful physical information from complex contact signals. Finally, we turn to robot learning: rather than assuming that more randomized data necessarily produces better policies, we propose the empirical Neural Tangent Kernel analysis to distinguish memorization, adaptation, and invariance to irrelevant variation. Across mechanism design, perception, and learning, we advance structure-conditioned intelligence: robot capabilities are shaped by physical structure in their bodies, their sensory representations, and their learning policies, enabling robust and generalizable manipulation. 

Biography

Chao Liu is an Assistant Professor in the Department of Mechanical Engineering and an Associate Member in the Department of Computer Science at The University of British Columbia, Vancouver. Liu was a Postdoctoral Associate at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT, hosted by Daniela Rus. Before this, Liu completed his Ph.D. advised by Mark Yim at the ModLab, a part of the General Robotics, Automation, Sensing & Perception (GRASP) Laboratory at the University of Pennsylvania. His research focuses on bio-inspired robots, swarm and modular robots, parallel robots, robotic control and motion planning, and grasping and manipulation through multimodal sensing. His work includes the hardware design of several robotic platforms, novel perception solutions, and efficient algorithms on motion planning and control. Liu was the finalist for the Best Paper Award on Safety, Security, and Rescue Robotics in Memory of Motohiro Kisoi at IROS 2019.