Wayne Chu

News

Recent updates on papers, research, and lab activities.

Mar. 2026

FINS, Efficient Construction of Implicit Surface Models From a Single Image for Motion Generation, was accepted to ICRA 2026 and is now available on arXiv.

Sep. 2025

I began my M.S. in Electrical Engineering at Stanford University, focusing on robotics and computer vision.

🌟 Highlight 🌟

Wayne dedicated much of his time to robotics and computer vision through projects, internships, and research. In the summer of 2023, he developed a project on dynamic obstacle avoidance using TurtleBot4 at UC San Diego, relying solely on depth camera inputs. His internship at ITRI further deepened his understanding of the challenges in bridging the Sim2Real gap, particularly in designing perception pipelines that remain robust in real-world conditions.

He conducted remote research in computer vision and robotics under the supervision of Prof. Weiming Zhi and Dr. Tianyi Zhang at the DROP Lab, CMU before beginning his studies at Stanford.

Autonomous driving and mobility project visual
FINS signed distance field reconstruction demo
Robotic arm surface-following demo

Education

Stanford University, CA

M.S. Student, Electrical Engineering

Concentration: Robotics / Computer Vision

  • GPA: N/A
  • Duration: Sep. 2025 - Apr. 2027 (Expected)

National Tsing Hua University, Taiwan

Bachelor of Science in Interdisciplinary Program of Engineering

Concentration: Electrical Engineering / Power Mechanical Engineering

  • Graduated with Distinction
  • GPA: 4.15 / 4.30
  • Duration: Sep. 2020 - Jan. 2024

Featured Research

Work Experience

Stanford Artificial Intelligence Laboratory logo

Stanford Artificial Intelligence Laboratory (SAIL)

Researcher @ Stanford Vision and Learning Lab

Sep. 2025 - In Progress

Current work:

  • Constructed a real-to-sim-to-real pipeline to generate controllable digital cousins and narrow the sim-to-real gap in robot learning policy.
  • Collected real-world data and developed a real-to-sim replay method.
Industrial Technology Research Institute logo

Industrial Technology Research Institute (ITRI)

Automotive AI Algorithm Development Intern

Sep. 2024 - Nov. 2024, Mar. 2025 - Jul. 2025

Mask2Former Semantic Segmentation

We combined and re-annotated Mapillary and ADE20k datasets to fulfill requirements for quadruped robots and autonomous vehicles. It was then converted to COCO format for the training of custom Mask2Former model using NVIDIA TAO Toolkit. Lastly, the re-trained model was integrated into ROS2 for real-time video inference on robotic and vehicle platforms at about 15 FPS.

Semantic segmentation demo on a quadruped robot

Person Re-Identification (Re-ID)

We built a real-time pedestrian detection and re-ID pipeline using YOLO11n and OSNet. The pipeline employed cosine similarity for feature matching and it exported models in .onnx and TensorRT .engine formats for model acceleration. We successfully achieved real-time inference at about 25 FPS for robotics and automotive use cases.

Person re-identification tracking demo

Sim2Real Quadruped Robot Terrain Traversal

We developed an elevation mapping workflow for quadruped robots Unitree Go2 by integrating Gazebo and point clouds from Intel RealSense depth camera. Moreover, a point cloud sampling & processing pipeline is integrated with reinforcement learning gait models in simulation. The most challenging part is to employed visual inertial odometry (VIO) to resolve sensor interruptions and implemented forward kinematics to compute foot coordinates of Unitree Go2. Finally, the system was deployed on Unitree Go2 for Sim2Real validation.

Unitree Go2 terrain traversal demo
Foxconn logo

Foxconn

Technology Innovation Group of Chairman Office Intern

Jun. 2024 - Sep. 2024

We designed an intuitive Android car app to control A/C temperature of the vehicle via voice. The pipeline was composed of Azure Speech Service → Dialogflow intent parsing → CarAPI to operate the HVAC system.

Selected Projects

Additional projects spanning robotics, embedded systems, and applied computer vision.

Publications

(† Corresponding author)

Efficient Construction of Implicit Surface Models From a Single Image for Motion Generation

Wei-Teng Chu, Tianyi Zhang, Matthew Johnson-Roberson, Weiming Zhi†

ICRA 2026 arXiv PDF
BibTeX
@misc{chu2025fins,
  title         = {Efficient Construction of Implicit Surface Models From a Single Image for Motion Generation}, 
  author        = {Wei-Teng Chu and Tianyi Zhang and Matthew Johnson-Roberson and Weiming Zhi},
  year          = {2025},
  eprint        = {2509.20681},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  url           = {https://arxiv.org/abs/2509.20681}, 
}
Plain Text
W.-T. Chu, T. Zhang, M. Johnson-Roberson, and W. Zhi,
"Efficient Construction of Implicit Surface Models From a Single Image for Motion Generation,"
arXiv preprint arXiv:2509.20681, 2025.
[Online]. Available: https://doi.org/10.48550/arXiv.2509.20681

Laser-Assisted Guidance Landing Technology for Drones

Yung-Ching Kuo, Wei-Teng Chu†, Yu-Po Cho, Jang-Ping Sheu

MASS 2024 PDF
BibTeX
@INPROCEEDINGS{10723664,
  author    = {Kuo, Yung-Ching and Chu, Wei-Teng and Cho, Yu-Po and Sheu, Jang-Ping},
  booktitle = {2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems (MASS)},
  title     = {Laser-Assisted Guidance Landing Technology for Drones},
  year      = {2024},
  pages     = {670-675},
  doi       = {10.1109/MASS62177.2024.00107}
}
Plain Text
Y. -C. Kuo, W. -T. Chu, Y. -P. Cho and J. -P. Sheu,
"Laser-Assisted Guidance Landing Technology for Drones,"
2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems (MASS),
Seoul, Korea, Republic of, 2024, pp. 670-675.
doi: 10.1109/MASS62177.2024.00107

Dodging Dynamical Obstacles Using Turtlebot4 Camera Feed

Wei-Teng Chu

PMAE 2023 Best Oral Presentation PDF
BibTeX
@InProceedings{10.1007/978-981-97-4806-8_17,
  author    = "Chu, Wei-Teng",
  editor    = "Mo, John P. T.",
  title     = "Dodging Dynamical Obstacles Using Turtlebot4 Camera Feed",
  booktitle = "Proceedings of the 10th International Conference on Mechanical, Automotive and Materials Engineering",
  year      = "2024",
  publisher = "Springer Nature Singapore",
  pages     = "195--204",
  isbn      = "978-981-97-4806-8"
}
Plain Text
Chu, WT. (2024). Dodging Dynamical Obstacles Using Turtlebot4 Camera Feed.
In: Mo, J.P.T. (eds) Proceedings of the 10th International Conference on Mechanical,
Automotive and Materials Engineering. CMAME 2023.
Lecture Notes in Mechanical Engineering. Springer, Singapore.
https://doi.org/10.1007/978-981-97-4806-8_17

Selected Awards

Academic Honors

Research Awards

  • 2024 The Excellence Award in the Undergraduate Research Competition
  • 2024 The 2nd Place in Undergraduate Research Poster Interpretation Competition
  • 2022 The Excellence Award in the Asia-Pacific Mechanics Contest for College Students

Scholarships

  • 2023, 2019 Dr. Chu Shun-I ZyXEL Scholarship (Top 15 in the Total of 2200 Seniors)
  • 2023 UC San Diego J. Yang Scholarship Award
  • 2023, 2022, 2021 NTHU & IPE Outgoing Exchange Student Scholarship
  • 2022 Jason International Charity Fund Scholarship Award by Acer Inc.