Hi there! I’m Gokul, a final-year PhD candidate in the Robotics Institute at Carnegie Mellon University, working on efficient algorithms for interactive learning (e.g. imitation / RL / RLHF).
My research focuses on developing the novel algorithmic paradigms required to build agents that are capable of achieving our desired outcomes, even when faced with situations unseen in their training data. In other words, rather than giving agents the “fish”, my research focuses on teaching agents to fish. I design practical algorithms that scale to domains like robotic manipulation and language modeling.
I work with Drew Bagnell and Steven Wu. I completed my B.S. / M.S. at UC Berkeley, where I worked with Anca Dragan on Learning with Humans in the Loop. I’ve spent summers working on ML @ SpaceX, Perception @ NVIDIA, Motion Planning @ Aurora, World Models @ Microsoft and LLMs @ Google.
In my free time, I like to consume baked goods, go to concerts / art museums, and run / lift. I’m a huge fan of birds (especially lovebirds), books (especially those by Murakami), and bands (especially Radiohead).
Events & News
🌟 June 2025 🌟 - Two new papers out on learning to search: one that introduces SAILOR: a method that outperforms diffusion policies trained on 10x as much human data on multi-stage visual manipulation tasks, another that allows real robots to avoid complex semantic failures via VLM verifiers (accepted to RSS ‘25).
🌟 March 2025 🌟 - New particularly exciting preprint out on the real value of RL in fine-tuning / RLHF. I gave a talk at Cornell on the paper that might also be of interest.
🌟 November 2024 🌟 - Drew, Steven, and I are co-teaching a course on the algorithmic foundations of interactive learning. If you’d like to understand the fundamental principles beyond imitation (e.g. for robots) and RLHF (e.g. for LLMs), this is the course for you!
Research Highlights
A Smooth Sea Never Made a Skilled 𝚂𝙰𝙸𝙻𝙾𝚁: Robust Imitation via Learning to Search
We introduce 𝚂𝙰𝙸𝙻𝙾𝚁: a method for learning to search from expert demonstrations that out-performs Diffusion Policies trained in 5-10x as much data on multi-stage visual manipulation tasks. [Paper] [Site]
All Roads Lead to Likelihood
We explore how the value of RL in fine-tuning / RLHF seems to be fundamentally derived from generation-verification gaps. [Paper] [Talk]
SPO: Self-Play Preference Optimization
We derive a new fundamental algorithm for RLHF that robustly handles the complex, intransitive preferences that often result from aggregating the a diversity of views. [Website] [Paper]
Inverse RL without RL
We derive exponentially faster algorithms for inverse RL by proving that local search, rather than global RL, is "all you need" for imitation. [Website][Paper][Podcast]
Of Moments and Matching
We provide a unifying, game-theoretic framework for imitation learning that explains when different algorithmic families can avoid compounding errors. [Website][Blog]