I am an MS CS at Stanford University, where I'm fortunate to be advised by Shuran Song as a member of Stanford REAL (Robotics and Embodied Artificial Intelligence Lab).
I received my BS EECS from UC Berkeley, where I was advised by Pieter Abbeel, Stephen James, and Xingyu Lin as a part of Berkeley RLL (Robot Learning Lab). I most recently spent time as an AI Resident at 1X, where I focused on training foundation policy models for humanoid robots.
My dream is for robots to become an everyday household occurrence; an important step is to enable robots to quickly adapt prior knowledge to new scenes or tasks. Towards this, I aim to answer two questions:
My recent interests are in capturing useful priors from large unstructured datasets for robot learning, such as motion and skills. Long term, I hope to leverage perspectives from cognitive science to inform how robots can learn from and like humans.
Chuan Wen*, Xingyu Lin*, John So*, Qi Dou, Kai Chen, Yang Gao, Pieter Abbeel
We learn to generate trajectories of arbitrary points from large actionless video datasets and condition a policy on these learned point trajectories, enabling sample-efficient policy learning and positive cross-embodiment transfer.
Xingyu Lin*, John So*, Sashwat Mahalingam, Fangchen Liu, Pieter Abbeel
We propose a novel method to adapt dense features from pre-trained vision backbones for sample-efficienct policy learning and generalization to unseen objects.
John So*, Amber Xie*, Sunggoo Jung, Jeffrey Edlund, Rohan Thakker, Ali Agha-mohammadi, Pieter Abbeel, Stephen James
We learn a segmentation model, train a navigation policy in simulation with RL in learned segmentation space, and deploy zero-shot to a real vehicle.
CS 221: Artificial Intelligence: Principles and Techniques — Fall 2024
CS 229: Machine Learning — Winter 2024
CS 189: Introduction to Machine Learning — Spring 2023
CS 61A: Structure and Interpretation of Computer Programs — Fall 2022, Spring 2022 (Head TA), Fall 2021
As an undergrad, I spent the majority of my time outside of research helping to build, organize, and lead Machine Learning at Berkeley (ML@B), serving as the organization's president in Fall 2022. We presented a white paper about our structure and initiatives at the NeurIPS 2022 Broadening Research Collaborations in ML Workshop; you may find a preprint here.
I like to think about how to best teach, learn, and optimize for fulfillment. Shoot me an email or DM if you'd like to chat :)