Action reasoning
My current work explores how to train large robot policies to learn from human video data.
Projects
Expressive Quadrupeds
My work explores how large language models (LLMs) and reinforcement learning from human feedback (RLHF) can be used to make quadrupeds like Pupper more friendly and expressive. Publication under review. 
I work for the nonprofit Hands-On Robotics, and we are collaborating with the Stanford Children’s Hospital to explore how quadrupeds can improve peri-operative and inpatient pediatric experience. Our work has been featured in the Stanford Report, ABC News, and more.
DeepShark
I initiated an international collaboration between Stanford Student Robotics and Universidad de Costa Rica to automate analysis of drone imagery of sharks, rays, and turtles. We developed a biometrics pipeline to automatically detect large marine animals and compute length, width, mass, and age. We were sponsored by the Stanford Doerr School of Sustainability and Parrot to lead a team of Stanford Students on a field mission to survey Santa Elena Bay, Costa Rica to survey endangered species (Pacific Nurse Sharks, Olive Ridley Turtles) and habitat (mangroves, reefs). Publication accepted, to be released in ECCV 2024 workshop proceedings. 
AI Robotics Education
In 2022 I co-taught and designed an introductory AI Robotics course to undergraduates at Stanford. In 2023, the course was elevated to CS 123 and I served as head TA for Professor Karen Liu. 
Previous: Online Re-planning for UAVs
Developed a new path repair algorithm GRIT (Greedy Repair Initializes Tabu search) for handling failures in multi-robot aerial surveys. GRIT outperforms path repair for nominal paths planned with both a traditional lawnmower-style planner and a more sophisticated integer program based planner, achieving adequate re-plans 10-50 times faster than two benchmark planners, making it ideal for online path repair in mid-flight. See the paper. 
Game Theoretic Planning
In the Stanford Multi-Robot Systems Lab, I am exploring how learned objectives can improve speed and precision of game-theoretic planners for automous vehicles. We published our most recent work here. 
Previous: Characterizing Key White Shark Habitat
White sharks are known to migrate across the Pacific Ocean to the lee of Hawai’i Island in the spring and early summer. Yet, little is known about why white sharks perform this migration and how key oceanographic features like eddies and seamounts affect their behavior. We analyzed novel white shark Pop-up Satellite Tag (PSAT) data tracking and performed in-situ sampling of oceanography in the region. Our results suggest that the core of anticyclonic eddies, peripheries of cyclonic eddies, and seamounts are structuring vibrant mesopelagic communities in the area, attracting white sharks in a similar way to the well-known White Shark Cafe. 
Previous: Gliding Lizard Flight Dynamics
Lizards of the genus Draco possess specialized morphological structures for gliding, including a patagium, throat lappets, and modified hindlimbs. Despite being among the most specialized reptilian gliders, it is currently unknown how Draco is able to maneuver effectively during flight. Here, we present a new computational method for characterizing the role of tail control on Draco glide distance and stability. We designed and printed a 3D Draco model, and estimated its lift and drag coefficients from wind tunnel experiments. Using the known mass and planar surface area of the Draco we developed a simplified, 3D simulation for Draco gliding, calculating longitudinal and lateral position and a pitch angle of the lizard with respect to a cartesian coordinate frame. Our model suggests an active tail improves both glide distance and stability in Draco. Our approach enables the replication and modification of existing gliders to better understand their performance and mechanics. This can be applied to extinct species, but also as a way of exploring the biomimetic potential of different morphological features. See the paper. 