Changliu Liu

Associate Professor Carnegie Mellon University

  • Pittsburgh PA

Contact

Carnegie Mellon University

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Biography

The ultimate goal of my research is to build intelligent and autonomous robots that think, behave and interact with the world in the way that human beings do, so that they can better serve, assist and collaborate with people in their daily lives across work, home and leisure.

The fundamental research question is to ensure those robots operate efficiently and safely in a human-involved environment. I am interested in addressing the microscopic aspect of the problem, e.g. the design of the behavior system (i.e. a mapping from observation to action) for single robot, as well as the macroscopic aspect of the problem, e.g. the analysis and validation of the human-robot system from a multi-agent perspective. Such human-robot system can be human-robot collaboration system in production lines, future transportation system with both automated and human-driven vehicles, or general cyber physical social system (CPSS).

Changliu Liu is an associate professor in the Robotics Institute, School of Computer Science, Carnegie Mellon University (CMU), where she leads the Intelligent Control Lab. Prior to joining CMU in Jan 2019, Dr. Liu was a postdoc at Stanford Intelligent Systems Laboratory in 2018.

Her research interests lie in the design and verification of human-centered intelligent systems with applications to manufacturing and transportation and on various robot embodiments, including robot arms, mobile robots, legged robots, and humanoid robots. Dr. Liu co-founded Instinct Robotics, a robotics company for intelligent manufacturing. Dr. Liu holds senior membership in IEEE, and membership in ASME and AAAI.

Areas of Expertise

Multi-Agent Systems
Robotics Foundations
Human-Robot Interaction
Motion Planning
Optimization

Media

Education

University of California at Berkeley

Ph.D.

Engineering

2017

University of California at Berkeley

M.S.

Engineering

University of California at Berkeley

M.S.

Mathematics

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Articles

Time-Optimal Trajectory Generation with Multi-level Continuous Kinodynamics Constraints

IEEE

Ruixuan Liu, Changliu Liu, Jessica Leu

2025-10-19

Time-optimal trajectory generation (TOTG) is critical in robotics applications to minimize travel time and increase robot task efficiency.

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VIRAL: Visual Sim-to-Real at Scale for Humanoid Loco-Manipulation

arXiv

Tairan He, Zi Wang, Haoru Xue, Qingwei Ben, Zhengyi Luo, Wenli Xiao, Ye Yuan, Xingye Da, Fernando Castañeda, Shankar Sastry, Changliu Liu, Guanya Shi, Linxi Fan, Yuke Zhu

2025-11-19

A key barrier to the real-world deployment of humanoid robots is the lack of autonomous loco-manipulation skills. We introduce VIRAL, a visual sim-to-real framework that learns humanoid loco-manipulation entirely in simulation and deploys it zero-shot to real hardware.

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Implicit Safe Set Algorithm for Provably Safe Reinforcement Learning

Journal of Artificial Intelligence Research

W Zhao, F Li, T He, C Liu

2025-12-14

Deep reinforcement learning (DRL) has demonstrated remarkable performance in many continuous control tasks. However, a significant obstacle to the real-world application of DRL is the lack of safety guarantees.

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