Professor Changliu Liu

Carnegie Mellon University

Office: 4525 Newell-Simon Hall

Personal Webpage


Administrative Assistant


Changliu Liu received her bachelor’s degrees in engineering and economics from Tsinghua University in 2012. She received her master’s degrees in engineering and mathematics from University of California, Berkeley in 2014 and 2016, respectively. She received her Ph.D. from University of California, Berkeley in 2017. She joined Stanford University as a postdoctoral fellow in 2018, after which she joined the Robotics Institute, School of Computer Science, Carnegie Mellon University as an assistant professor.

Prof. Liu’s research interests include robotics and human robot interactions, control and motion planning, optimization and optimal control, multi-agent system and game theory.

16-899 - Adaptive control and reinforcement learning

This course will discuss algorithms that learn and adapt to the environment. This course is directed to students—primarily graduate although talented undergraduates are welcome as well—interested in developing adaptive software that makes decisions that affect the world. This course will discuss adaptive behaviors both from the control perspective and the learning perspective.

Course Link

16-883 - Provably safe robotics

Safe autonomy has become increasingly critical in many application domains. It is important to ensure not only the safety of the ego robot, but also the safety of other agents (humans or robots) that directly interact with the autonomy. For example, robots should be safe to human workers in human-robot collaborative assembly; autonomous vehicles should be safe to other road participants. For complex autonomous systems with many degrees of freedom, safe operation depends on the correct functioning of all system components, i.e., accurate perception, optimal decision making, and safe control. This course deals with both the design and the verification of safe robotic systems. From the design perspective, we will talk about how to assure safety through planning, prediction, learning, and control. From the verification perspective, we will talk about verification of deep neural networks, safety or reachability analysis for closed loop systems, and analysis of multi-agent systems.

Course Link