I am currently a robotics researcher at ByteDance Seed-Robotics, where my research focuses on robotic reinforcement learning, learning based robot manipulation and locomotion, and unified control frameworks for robots. Previously, I worked as a researcher at Tencent Robotics X, with research interests in robot motion control and planning, high-dynamic motion planning, and real-time dynamic parameters identification for robotic systems. I hold both a bachelor’s and a master’s degree from the Beijing Advanced Innovation Center for Intelligent Robots and Systems at Beijing Institute of Technology, where I was supervised by Professors Zhihong Jiang and Qiang Huang.

I am deeply passionate about robotics, with my career consistently centered on robot control and planning. During my tenure at Tencent, I concentrated on real-time control and planning grounded in dynamics and numerical optimization. At ByteDance, I have conducted in-depth research on end-to-end control methods for large-scale neural networks trained using supervised learning and reinforcement learning. My long-term ambition is to develop general-purpose embodied robots with human-like intelligence, enabling robots to seamlessly integrate into human production and daily life, and ultimately drive meaningful progress in human society.

Contact Me: If you are interested in collaborating with me or discussing any research questions, please feel free to drop me an email.

Research Interests

My research focuses at the intersection of embodied intelligence and robotics, particularly the following areas:

• Robot Manipulation: long-horizon manipulation, dexterous manipulation, deformable manipulation, etc.
• Robot Locomotion: optimal control, dynamics and model-based control, RL-based legged locomotion, etc.
• System Identification: system modeling, online dynamic parameters identification, offline kinematics calibration, etc.
• System Infrastructure: control system and core algorithm, data collection and annotation system, deployment optimization, etc.