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.

Zeyi Liu ,Shuang Li ,Eric Cousineau ,Siyuan Feng ,Benjamin Burchfiel ,Shuran Song
In submission
Website  •   ArXiv  •   Code

TL;DR: A 4D video generation model that enforces geometric consistency across views to generate spatio-temporally aligned RGB-D sequences, enabling downstream applications in robot manipulation tasks via pose tracking.

Xiaomeng Xu* ,Yifan Hou* ,Chendong Xin ,Zeyi Liu ,Shuran Song
Neural Information Processing Systems (NeurIPS), 2025
CoRL 2025 Workshop on Sensorizing, Modeling, and Learning from Humans (Best Paper Award)
Website  •   ArXiv  •   Code

TL;DR: Improving contact-rich manipulation with a compliant residual policy learned from on-policy delta human correction.

Yifan Hou ,Zeyi Liu ,Cheng Chi ,Eric Cousineau ,Naveen Kuppuswamy ,Siyuan Feng ,Benjamin Burchfiel ,Shuran Song
IEEE International Conference on Robotics and Automation (ICRA), 2025
ICRA 2025 Workshop on Learning Meets Model-Based Methods for Contact-Rich Manipulation (Best Paper Award)
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 2024 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 2023 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.