Tong Xu

Tong Xu

PhD student for Robotics

George Mason University

RobotiXX Lab

Hello there!

I am currently a senior PhD student in RobotiXX Lab at George Mason University, advised by Prof. Xuesu Xiao. I received my master’s degree from University of Southern California and bachelor’s degree from Nanjing University of Information Science & Technology.

My primary research interests include motion planning, reinforcement learning, and whole-body loco-manipulation. My current work focuses on kinodynamics adaptation across heterogeneous autonomous robot fleets and humanoid robot learning.

Interests

  • Robotics
  • Motion Planning
  • Reinforcement Learning
  • Foundation Models

Education

  • Ph.D. in Computer Science, 2023

    George Mason University

  • M.S. in Computer Science, 2021

    University of Southern California

  • B.E. in Network Engineering, 2017

    Nanjing University of Information Science & Technology

News

Publications

Journal

Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned from The Forth BARN Challenge at ICRA 2025

Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned from The Forth BARN Challenge at ICRA 2025

CARoL: Context-aware Adaptation for Robot Learning

CARoL: Context-aware Adaptation for Robot Learning

Pietra: Physics-informed evidential learning for traversing out-of-distribution terrain

Pietra: Physics-informed evidential learning for traversing out-of-distribution terrain

Conference

VertiAdaptor: Online Kinodynamics Adaptation for Vertically Challenging Terrain

VertiAdaptor: Online Kinodynamics Adaptation for Vertically Challenging Terrain

Traverse the Non-Traversable: Estimating Traversability for Wheeled Mobility on Vertically Challenging Terrain

Traverse the Non-Traversable: Estimating Traversability for Wheeled Mobility on Vertically Challenging Terrain

Adaptive Dynamics Planning for Robot Navigation

Adaptive Dynamics Planning for Robot Navigation

Verti-Arena: A Controllable and Standardized Indoor Testbed for Multi-Terrain Off-Road Autonomy

Verti-Arena: A Controllable and Standardized Indoor Testbed for Multi-Terrain Off-Road Autonomy

Decremental Dynamics Planning for Robot Navigation

Decremental Dynamics Planning for Robot Navigation

Reward Training Wheels: Adaptive Auxiliary Rewards for Robotics Reinforcement Learning

Reward Training Wheels: Adaptive Auxiliary Rewards for Robotics Reinforcement Learning

VertiSelector: Automatic Curriculum Learning for Wheeled Mobility on Vertically Challenging Terrain

VertiSelector: Automatic Curriculum Learning for Wheeled Mobility on Vertically Challenging Terrain

Verti-Bench: A General and Scalable Off-Road Mobility Benchmark for Vertically Challenging Terrain

Verti-Bench: A General and Scalable Off-Road Mobility Benchmark for Vertically Challenging Terrain

Reinforcement learning for wheeled mobility on vertically challenging terrain

Reinforcement learning for wheeled mobility on vertically challenging terrain

Preprint

VertiAKD: Adaptive Off-Road Kinodynamics on Vertically Challenging Terrain

VertiAKD: Adaptive Off-Road Kinodynamics on Vertically Challenging Terrain

Experience

 
 
 
 
 
RobotiXX Logo

Graduate Research Assistant

RobotiXX

May 2024 – Present Fairfax
  • Research in off-road navigation, reinforcement learning, and foundation models
  • Leading the Verti-Bench for rapid kinodynamics adaptation cross different types of vehicles in off-road navigation scenario
 
 
 
 
 
George Mason University Logo

Graduate Teaching Assistant

George Mason University

Aug 2023 – May 2025 Fairfax
  • Designed student lab contents involving data structure, led weekly lab recitations and office hours
  • Created grading scripts and managed a team of 8 undergraduate teaching assistants
 
 
 
 
 
H2X Lab Logo

Research Intern

H2X Lab

May 2022 – Aug 2022 Boston
  • DeepVO - Visual Odometry with Deep Learning
  • OpenGuide - A Scalable Human-Like Guidance System for Travelers with Visual Impairment

Projects

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DeepVO

Reproduced Deep Visual Odometry architecture by integrating a pretrained FlowNetSimple model with LSTM. Achieved 14.8% performance improvement through epoch optimization and 8.4% improvement through hyperparameter tuning in the loss function, evaluated on KITTI and nuScenes datasets using translation and rotation RMSE metrics.

Cone Detection

Implemented Faster R-CNN for cone detection by integrating Region Proposal Network with Fast R-CNN. Achieved 37.4% higher recall rate compared to YOLOv3 on a cone-annotated dataset, demonstrating superior detection performance.

OpenGuide

A Scalable Human-Like Guidance System for Travelers with Visual Impairment.

Between World

A 2D Unity platformer game where players switch between parallel worlds to solve traversal puzzles, avoid hazards, and progress through multi-stage levels.

Contact

  • txu25@gmu.edu
  • 4400 University Dr, Fairfax, VA 22030
  • RobotiXX Lab