Advancing Intelligent Robotics Through Learning and Control
Our lab develops both foundational theory and practical tools in machine learning and control to make robots more intelligent.
On the one hand, reinforcement learning provides a data-driven way for robots to learn decision-making policies through interaction. On the other hand, control theory offers rigorous, reliable design principles that guarantee stability and performance. By combining them, we enable robots to operate safely, autonomously, and efficiently in complex, real-world environments.
We are actively recruiting undergraduate and graduate students to join our team. Please see CONTACT for more information.
Highlights
Our Research
Our research can be broadly organized into three parts: learning for control, control for learning, and applications to robotics.
Our Teaching
We offer courses in the areas of dynamics & control and robotics.
Our Team
Our team includes researchers and graduate/undergraduate students passionate about learning and control and their applications to robotics.
News and Updates
- [July 15, 2026] — We are excited to announce that the LC Lab has welcomed a new Unitree G1 humanoid robot! The robot will serve as an important experimental platform for our research in machine learning, control, and intelligent robotics.
- [Summer 2026] — Rebecca joined the LC Lab as a summer researcher. During her time with the lab, she will contribute to ongoing research projects and gain hands-on experience in learning, control, and robotics.
- [Fall 2026] — An Nguyen joined the LC Lab as a new PhD student. We are delighted to welcome him to the team and look forward to his research contributions.
Last updated: July 19, 2026