Tao Zhong

I'm a Ph.D. student at Princeton University, supervised by Prof. Christine Allen-Blanchette. I received my B.A.Sc. in Engineering Science from the University of Toronto, where I worked with Prof. Animesh Garg. Previously, I interned at Noah's Ark Lab Canada with Prof. Yang Wang on computer vision and domain generalization, and worked with Prof. Huihuan Qian at AIRS and CUHK(SZ) on marine robotics.

Feel free to check out my CV or send me an Email if you want to connect.

Email  /  CV  /  Scholar  /  LinkedIn  /  Github

profile photo

Research

My primary research interests lie at the intersection of robotics and computer vision.

Grasp2Grasp: Vision-Based Dexterous Grasp Translation via Schrödinger Bridges
Tao Zhong, Jonah Buchanan, Christine Allen-Blanchette
Preprint, 2025
project page / arXiv

Grasp2Grasp enables simulation-free, vision-based translation of dexterous grasps across robot hands using Schrödinger Bridges with physics-informed costs for stable, functionally aligned grasps.

GAGrasp: Geometric Algebra Diffusion for Dexterous Grasping
Tao Zhong, Christine Allen-Blanchette
ICRA, 2025
project page / arXiv

GAGrasp uses a geometric algebra diffusion model to generate robust, physically plausible dexterous grasps that are naturally equivariant to an object's pose.

Adapting to Distribution Shift by Visual Domain Prompt Generation
Zhixiang Chi*, Li Gu*, Tao Zhong, Huan Liu, Yuanhao Yu, Konstantinos N Plataniotis, Yang Wang
ICLR, 2024
project page / arXiv

We introduce VDPG, a method that adapts large models to new visual domains at test-time using only a few unlabeled images to generate a domain-specific prompt that guides the model's features.

Fast-Grasp'D: Dexterous Multi-finger Grasp Generation Through Differentiable Simulation
Dylan Turpin, Tao Zhong, Shutong Zhang, Guanglei Zhu, Eric Heiden, Miles Macklin, Stavros Tsogkas, Sven Dickinson, Animesh Garg
ICRA, 2023
project page / arXiv

This paper presents Fast-Grasp'D, a differentiable simulator that rapidly generates Grasp'D-1M, a large dataset of stable, contact-rich, multi-finger grasps for robotic learning.

Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts
Tao Zhong*, Zhixiang Chi*, Li Gu*, Yang Wang, Yuanhao Yu, Jin Tang
NeurIPS, 2022
arXiv / Code

We propose Meta-DMoE, a framework that adapts to domain shifts by using a meta-learned aggregator to distill knowledge from specialized expert models into a student network for fast test-time adaptation.

Education

Princeton University
Ph.D. in Mechanical and Aerospace Engineering (Robotics Track)
2023.09 - Present

cGPA: 4.0
University of Toronto
B.A.Sc. in Engineering Science with High Honours
Major in Robotics Engineering, Minor in Artificial Intelligence
2018.09 - 2023.06

cGPA: 3.81

Miscellanea

Academic Service

Reviewer, NeurIPS (2025), RA-L, ICLR (2025), L4DC (2024)

Teaching

Teaching Assistant, MAE433 Fall 2025

Website source code adapted from Jon Barron's template.