I'm a first-year Computer Science PhD at UC Berkeley, advised by Pieter Abbeel.
I recently graduated from Stanford University with a distinction in research.
During my masters, I was an intern in the Robotics team at Google Brain and an AI resident at Everyday Robots.
My research centers around robotic perception and manipulation. In particular, I'm interested in endowing robots with the flexible ability to learn from rich sources of data, like video demonstrations and natural language instructions.
XIRL: Cross-embodiment Inverse Reinforcement Learning
Kevin Zakka, Andy Zeng, Pete Florence, Jonathan Tompson, Jeannette Bohg, Debdidatta Dwibedi
Conference on Robot Learning (CoRL), 2021 (Oral Presentation)
★ Best Paper Award Finalist
Form2Fit: Learning Shape Priors for Generalizable Assembly from Disassembly
Kevin Zakka, Andy Zeng, Johnny Lee, Shuran Song
IEEE International Conference on Robotics and Automation (ICRA) 2020
★ Best Paper Award in Automation Finalist
I actively contribute to MuJoCo, a popular and widely used physics simulator for robotics and control.
Some representative software I've developed over the years:
: a tool for converting OBJ files into multiple MuJoCo meshes grouped by material
: software and tasks for dexterous hand manipulation, powered by MuJoCo
: colab notebooks showcasing CLIP's impressive zero-shot capabilities
: numerical linear algebra routines from scratch in pure python