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.

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Research

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

We introduce LEGO, a symmetry-aware graph neural network framework that enables sample-efficient, scalable, and generalizable swarm robot control across diverse team sizes and environments.

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

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.

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.

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

Miscellanea