Picture taken by Tina Lin

Hi, I'm a third year PhD student at Stanford University, advised by Professor Shuran Song and part of the REAL lab. Previously, I was an undergraduate student at Columbia University studying Computer Science and Applied Math.

My work focuses on robot perception and manipulation. More specifically, I'm interested in developing methods for embodied agents to better perceive and understand the environment through multimodal data (e.g. vision, language, audio), which facilitates learning of robust and generalizable policies.

Yifan Hou Zeyi Liu ,Cheng Chi ,Eric Cousineau ,Naveen Kuppuswamy ,Siyuan Feng ,Benjamin Burchfiel ,Shuran Song
In Submission to IEEE International Conference on Robotics and Automation (ICRA), 2025
Website  •   ArXiv  •   Code

TL;DR: Levergae diffusion model to learn adaptive compliance control for contact-rich tasks from vision and force feedback.

Zeyi Liu ,Cheng Chi ,Eric Cousineau ,Naveen Kuppuswamy ,Benjamin Burchfiel ,Shuran Song
Conference on Robot Learning (CoRL), November 2024
CoRL Workshop on Mastering Robot Manipulation in a World of Abundant Data (Oral presentation)
Website  •   ArXiv  •   Video  •   Code  •   MIT Tech Review

TL;DR: A data collection and policy learning framework that learns contact-rich robot manipulation skills from in-the-wild audio-visual data.

Zixi Wang ,Zeyi Liu ,Nicolas Ouporov ,Shuran Song
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2024 Website  •   ArXiv  •   Code (coming soon)

TL;DR: A robot to human handover system that leverages human contact points to inform robot grasp and object delivery pose.

Zeyi Liu* ,Arpit Bahety* ,Shuran Song
Conference on Robot Learning (CoRL), November 2023
CoRL Workshop on Language and Robot Learning (Oral presentation)
Website  •   ArXiv  •   Video  •   Code

TL;DR: A framework that leverages LLM for robot failure explanation and correction, based on a hierarchical summary of robot past experiences generated from multisensory data.

Zeyi Liu ,Zhenjia Xu ,Shuran Song
Conference on Robot Learning (CoRL), December 2022
Website  •   ArXiv  •   Video  •   Code

TL;DR: A toy-inspired simulated learning environment for embodied agents to acquire object manipulation, inter-object relation reasoning, and goal-conditioned planning skills.