I recently joined Prof. Xiaolong Wang's lab at UC San Diego, where I am working on robot learning research. My current project involves leveraging real-world interaction data to improve the fidelity of simulation, with the goal of enabling more reliable transfer of learned policies to physical hardware.

This research investigates the "sim-to-real" gap, where AI agents trained in simulation falter in the real world. I benchmarked model-based (SHAC) versus model-free (SAC, PPO) reinforcement learning algorithms on their ability to adapt to changes in physics and sensor noise in the classic Pendulum control task. My experiments revealed that the model-free SAC algorithm was not only more robust to these variations but also learned an expert policy five times faster than the other methods.

This research explores how well AI agents, trained by watching expert demonstrations, can adapt to unexpected changes in their environment. I conducted a comprehensive study comparing three key imitation learning algorithms (BC, GAIL, and AIRL) across several simulated robotic tasks with altered physics and goals. The findings reveal the limitations of current methods in generalizing to novel situations and highlight the challenges of transferring a learned understanding of a task's objective.