Introduction


Welcome to the Physical Intelligence Lab! Here, we merge the realms of robotics and machine learning to explore innovative solutions that enhance physical interaction with the world. Our research delves into intelligent systems that not only perform tasks but also learn and adapt in real-time, paving the way for advancements in automation, smart environments, and beyond.

Latest News

Our work is accepted by ICRA 2025.

We are excited to announce that our latest research titled “Beyond Robustness: Learning Unknown Dynamic Load Adaptation for Quadruped Locomotion on Rough Terrain” has been accepted by ICRA 2025.

Mar 6, 2025 1 min read
Our work is accepted by ICRA 2025.
Two of our recent works are accepted by RA-L.

We are pleased to announce that our latest work “CTS: Concurrent Teacher-Student Reinforcement Learning for Legged Locomotion” and “Learning Whole-Body Loco-Manipulation for Omni-Directional Task Space Pose Tracking with a Wheeled-Quadrupedal-Manipulator” has been accepted by IEEE Robotics and Automation Letters (RA-L).

Dec 1, 2024 2 min read
Two of our recent works are accepted by RA-L.
Dr. Hua Chen joined the ZJU-UIUC Institute as Tenure-track Assistant Professor

Warm congratulations to Dr. Hua Chen on joining the ZJU-UIUC Institute as a tenure-track Assistant Professor!

Mar 1, 2024 1 min read
Dr. Hua Chen joined the ZJU-UIUC Institute as Tenure-track Assistant Professor

Featured Publications

Beyond Robustness: Learning Unknown Dynamic Load Adaptation for Quadruped Locomotion on Rough Terrain
Project Page
Beyond Robustness: Learning Unknown Dynamic Load Adaptation for Quadruped Locomotion on Rough Terrain
Learning Whole-Body Loco-Manipulation for Omni-Directional Task Space Pose Tracking With a Wheeled-Quadrupedal-Manipulator
Cite Arxiv Project Page
Learning Whole-Body Loco-Manipulation for Omni-Directional Task Space Pose Tracking With a Wheeled-Quadrupedal-Manipulator
CTS: Concurrent Teacher-Student Reinforcement Learning for Legged Locomotion
Cite Arxiv Project Page
CTS: Concurrent Teacher-Student Reinforcement Learning for Legged Locomotion