Previously, I worked on human data and real-to-sim for robot learning at Tesla Optimus, and policy learning and evaluation at 1X Technologies.
Before that, I received my MS CS at Stanford advised by Shuran Song as a member of Stanford REAL (Robotics and Embodied Artificial Intelligence Lab), and my BS EECS from UC Berkeley advised by Pieter Abbeel, Stephen James, and Xingyu Lin as a part of Berkeley RLL (Robot Learning Lab).
My dream is for robots to become an everyday household occurrence; towards this end, I believe that large scale data is fundamental to bridge the gap between dexterity and robust generalization to unseen scenarios.
Nowadays, I aim to answer how to collect and extract these priors using scalable data sources, including human data collection, video, and simulation.
Long term, I hope to leverage perspectives from cognitive science and developmental psychology to inform how robots can learn from and like humans.
Chuan Wen*, Xingyu Lin*, John So*, Qi Dou, Kai Chen, Yang Gao, Pieter Abbeel
TL;DR: We condition a policy on arbitrary point trajectories learned from actionless videos, enabling sample-efficient policy learning and positive cross-embodiment transfer.
Xingyu Lin*, John So*, Sashwat Mahalingam, Fangchen Liu, Pieter Abbeel
TL;DR: We propose a method to adapt dense features from pre-trained vision backbones for visuomotor policies, enabling sample-efficient generalization to unseen objects.
John So*, Amber Xie*, Sunggoo Jung, Jeffrey Edlund, Rohan Thakker, Ali Agha-mohammadi, Pieter Abbeel, Stephen James
TL;DR: We train a navigation policy in simulation with RL in learned segmentation space, and deploy zero-shot to a real vehicle.
I'm forever grateful to the communities which raised me, notably Machine Learning at Berkeley (ML@B), CS 61A Staff, Accel Scholars, and Felicis Fellows.
In particular, I spent the majority of my undergrad learning from and organizing ML@B, serving as its president in 2022. We presented a white paper about our structure and initiatives at the NeurIPS 2022 Broadening Research Collaborations in ML Workshop.
I like to think about how to best teach, learn, and optimize for fulfillment. Rarely, I write my thoughts down. Shoot me an email or DM if you'd like to chat. 🤠