Zhengtong Xu 徐政通

Email: xu1703 AT purdue.edu

I'm a third-year PhD Student at Purdue University, advised by Professor Yu She.

I received my Bachelor's degree in mechanical engineering at Huazhong University of Science and Technology.

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News

  • [Sep. 2024] One paper accepted by IEEE T-RO
  • [Aug. 2024] Entered the third year of my PhD

Research

My research aims to develop robots capable of performing everyday manipulation tasks with human-level capabilities. Towards this goal, my focus is on generative models in robotics, imitation learning, and multi-sensory robot manipulation.

LeTac-MPC: Learning Model Predictive Control for Tactile-reactive Grasping
Zhengtong Xu, Yu She
Accepted by IEEE T-RO, 2024

arXiv / video / code / bibtex

A generalizable end-to-end tactile-reactive grasping controller with differentiable MPC, combining learning and model-based approaches.

UniT: Unified Tactile Representation for Robot Learning
Zhengtong Xu, Raghava Uppuluri, Xinwei Zhang, Cael Fitch, Philip Glen Crandall, Wan Shou, Dongyi Wang, Yu She
Under Review, 2024

website / arXiv / video / code / bibtex

Learn a unified tactile representation with transferability and generalizability only by a single simple object.

VILP: Imitation Learning with Latent Video Planning
Zhengtong Xu, Qiang Qiu, Yu She
Under Review, 2024

arXiv(soon) / video(soon) / code

VILP integrates the video generation model into policies, enabling the representation of multi-modal action distributions while reducing reliance on extensive high-quality robot action data.

LeTO: Learning Constrained Visuomotor Policy with Differentiable Trajectory Optimization
Zhengtong Xu, Yu She
Under Review, 2024

arXiv / video / code / bibtex

LeTO is a "gray box" method which marries optimization-based safety and interpretability with representational abilities of neural networks.

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VisTac: Toward a Unified Multimodal Sensing Finger for Robotic Manipulation
Sheeraz Athar*, Gaurav Patel*, Zhengtong Xu, Qiang Qiu, Yu She
IEEE Sensors Journal, 2023

paper / video / bibtex

VisTac seamlessly combines high-resolution tactile and visual perception in a single unified device.

Awards

  • Dr. Theodore J. and Isabel M. Williams Fellowship, Purdue University, 2022
  • Chinese National Scholarship, Ministry of Education of China, 2017

Teaching

  • Vertically Integrated Projects (VIP)-GE Robotics and Autonomous Systems, Grad Mentor, Spring 2024/Fall 2023/Summer 2023
  • IE 474-Industrial Control Systems, Teaching Assistant, Fall 2022

Website template from Jon Barron's website.