Safe Control and Learning for Effective Human-Robot Collaboration
In this talk, I will discuss our recent work on safe control and learning for effective human-robot collaboration. I will first introduce safe control methods using energy-function-based methods, then discuss how to combine them with learning controllers where an explicit analytical dynamic model of the system is usually not available (especially in human-robot systems). These safe control methods will enable safe reinforcement learning with zero training time violation. Then I will discuss about methods to robustly learn models to predict human behaviors. The key challenge we need to address is the distribution shift between the offline collected human behavioral data and the online measured human behaviors. To mitigate the distribution shift, we introduce two methods: online model adaptation, and offline verification-guided data augmentation. These methods have been applied to facilitate human-robot collaboration in industrial assembly tasks. I will conclude the talk with future visions on how to effectively deploy human-robot systems on factory floors.
Dr. Changliu Liu
Assistant Professor, Robotics Institute, School of Computer Science,
Carnegie Mellon University (CMU)
Dr. Changliu Liu is an assistant professor in the Robotics Institute, School of Computer Science, Carnegie Mellon University (CMU), where she leads the Intelligent Control Lab. Prior to joining CMU, Dr. Liu was a postdoc at Stanford Intelligent Systems Laboratory. She received her Ph.D. from University of California at Berkeley and her bachelor’s degrees in engineering and economics from Tsinghua University. Her research interests lie in the design and verification of intelligent systems with applications to manufacturing and transportation. She published the book “Designing robot behavior in human-robot interactions” with CRC Press in 2019. She received many best paper awards, Rising Star in EECS, Amazon Research Award, and Ford URP Award.
Host: Professor Alex Wyglinski