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.
|
|
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
|
|