Zhengtong Xu 徐政通

Email: xu1703 AT purdue.edu

I'm a third-year PhD candidate 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

Research

* indicates equal contribution.

My research aims to design learning algorithms for robotic agents, enabling them to perform everyday manipulation tasks with human-level proficiency. To this end, I am currently focusing on hierarchical multimodal robot learning. Specifically, my research explores:

1. Integrating visual, 3D, and tactile modalities for robot learning.
2. Combining differentiable optimization and learning for interpretable, reactive low-level robot policies.
3. Deploying pretrained vision-language models for high-level reasoning and planning.

LeTac-MPC: Learning Model Predictive Control for Tactile-reactive Grasping
Zhengtong Xu, Yu She
IEEE Transactions on Robotics (T-RO), 2024

                    @article{xu2024letac,
                      author={Xu, Zhengtong and She, Yu},
                      journal={IEEE Transactions on Robotics}, 
                      title={{LeTac-MPC}: Learning Model Predictive Control for Tactile-Reactive Grasping}, 
                      year={2024},
                      volume={40},
                      number={},
                      pages={4376-4395},
                      doi={10.1109/TRO.2024.3463470}
                    }
                  

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

UniT: Data Efficient Tactile Representation with Generalization to Unseen Objects
Zhengtong Xu, Raghava Uppuluri, Xinwei Zhang, Cael Fitch, Philip Glen Crandall, Wan Shou, Dongyi Wang, Yu She
IEEE Robotics and Automation Letters (RA-L), 2025

                    @article{xu2024unit,
                      title={UniT: Unified Tactile Representation for Robot Learning},
                      author={Xu, Zhengtong and Uppuluri, Raghava and Zhang, Xinwei and Fitch, Cael and Crandall, Philip Glen and Shou, Wan and Wang, Dongyi and She, Yu},
                      journal={arXiv preprint arXiv:2408.06481},
                      year={2024}
                    }
                  

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

Safe Human-Robot Collaboration with Risk-tunable Control Barrier Functions
Vipul K. Sharma*, Pokuang Zhou*, Zhengtong Xu*, Yu She, S. Sivaranjani
Under Review, 2025

We consider the problem of guaranteeing safety constraint satisfaction in human-robot collaboration with uncertain human position. We pose this problem as a chance-constrained problem with safety constraints represented by uncertain control barrier functions.

VILP: Imitation Learning with Latent Video Planning
Zhengtong Xu, Qiang Qiu, Yu She
IEEE Robotics and Automation Letters (RA-L), 2025

                    @misc{xu2025vilp,
                      title={VILP: Imitation Learning with Latent Video Planning}, 
                      author={Zhengtong Xu and Qiang Qiu and Yu She},
                      year={2025},
                      eprint={2502.01784},
                      archivePrefix={arXiv},
                      primaryClass={cs.RO},
                      url={https://arxiv.org/abs/2502.01784}, 
                    }
                  

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
IEEE Transactions on Automation Science and Engineering (T-ASE), 2024

                    @article{xu2024leto,
                      title={LeTO: Learning Constrained Visuomotor Policy with Differentiable Trajectory Optimization},
                      author={Xu, Zhengtong and She, Yu},
                      journal={arXiv preprint arXiv:2401.17500},
                      year={2024}
                    }
                  

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

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

                    @article{athar2023vistac,
                      title={Vistac towards a unified multi-modal sensing finger for robotic manipulation},
                      author={Athar, Sheeraz and Patel, Gaurav and Xu, Zhengtong and Qiu, Qiang and She, Yu},
                      journal={IEEE Sensors Journal},
                      year={2023},
                      publisher={IEEE}
                    }
                  

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

Awards

• Magoon Graduate Student Research Excellence Award (sole awardee from Purdue IE), Purdue University, 2025
• Dr. Theodore J. and Isabel M. Williams Fellowship, Purdue University, 2022
• Chinese National Scholarship, Ministry of Education of China, 2017

Reviewer Service

• IEEE Robotics and Automation Letters (RA-L), 2025
• IEEE Transactions on Robotics (T-RO), 2024
• IEEE International Conference on Robotics and Automation (ICRA), 2024

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.