Peilin Wu 吴沛琳

Hello! I am an undergraduate student of the IEEE Honor Class in Shanghai Jiao Tong University (SJTU), majoring in Computer Science and Technology. Currently, I am a member of Apex Data & Knowledge Management Lab, advised by Prof. Weinan Zhang.

Generally, I work on robotics and reinforcement learning. My previous works center on implementing learning-based techniques to handle the policy adaptation problem in the quadruped locomotion field. For future research, I'm now focusing on general robotic topics such as:

• How can robots benefit from their knowledge of embodiment?
• How to leverage the power of foundation models in robotics?
• How can robots adapt to versatile tasks and environments through data collection and generation?

CV  /  Email  /  GitHub  /  Google Scholar  

Publications

I'm interested in robotics, reinforcement learning and deep learning.

LoopSR: Looping Sim-and-Real for Lifelong Policy Adaptation of Legged Robots

This project looked into the lifelong policy adaptation situation. The idea behind was that attention should be paid to leveraging real-world data to fine-tune the policy continuously. The work proposed a pipeline to loop simulated training and real-world data collection, with only a limited amount of data to yield eminent performance in both sim-to-sim and sim-to-real experiments.

Bridging the Sim-to-Real Gap from the Information Bottleneck Perspective

This paper focused on modeling the privileged knowledge distillation problem from a theory-based perspective, where we provided mathematical analysis and a simple but effective framework HIB for the problem. Empirical experiments on both simulated and real-world tasks demonstrate that HIB yields improved generalizability compared to previous methods, which achieved about a 10% performance boost compared with baselines in various tasks.

Services

Design and source code from Jon Barron's website