Patrick Yin

I am a PhD student at the University of Washington advised by Professor Abhishek Gupta. My research is graciously supported by the NSF Graduate Research Fellowship. I'm broadly interested in embodied AI and building intelligent robots.

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We propose Simulation-Guided Fine-Tuning (SGFT) - a simple, general sim2real framework which extracts structured exploration priors from simulation to accelerate real world RL.

A large, diverse robot manipulation dataset with 76k demonstration trajectories.

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 Planning to Practice (PTP), a method which makes it practical to train goal-conditioned policies for long-horizon tasks.

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.