I am a PhD student at the University of Washington advised by Professor
Abhishek Gupta.
I'm broadly interested in embodied AI and building intelligent robots.
We propose a learning system that can leverage a small amount of real-world data to autonomously refine a simulation model, enabling sim-to-real transfer for real-world robotic manipulation tasks.
We discover that a shallow and wide architecture can boost the performance of contrastive RL approaches on simulated benchmarks.
Additionally, we demonstrate that contrastive approaches can solve real-world robotic manipulation tasks.
A large, open-source real robot dataset with 1M+ real robot trajectories spanning 22 robot embodiments,
from single robot arms to bi-manual robots and quadrupeds.
We propose Fine-Tuning with Lossy Affordance Planner (FLAP), a framework that
leverages diverse offline data for learning representations, goal-conditioned policies, and affordance
models that enable rapid fine-tuning to new tasks in target scenes.
We propose a new form of state abstraction called goal-conditioned bisimulation that captures functional equivariance, allowing for the reuse of skills to achieve new goals in goal-conditioned reinforcement learning.
Miscellaneous from Undergrad
Notes that I took on machine learning, math, and books during undergrad