Chen (Albert) Feng (冯宸)

I am a Ph.D. candidate in UAV Group, ECE at HKUST, supervised by Prof. Shaojie Shen. Prior to HKUST, I obtained a B.Eng from Harbin Institute of Technology and majored in Mechatronics Engineering (ME) in 2021.

I was a research intern from May. 2021 to Sept. 2021 in Video Group at Megvii Research of Megvii (Beijing, China). In my undergraduate study, I was an team member of computer vision group and mechanics group in Harbin Institute of Technology Competition Robotics Team (HITCRT) (Harbin, China).

Email / Google Scholar / Github / CV


  • Feb 2024: A paper is accepted to RA-L.
  • Jan 2024: Three papers are accepted to ICRA 2024.
  • Dec 2023: I have passed Ph.D. Qualifying Examination to become a Ph.D. candidate.
  • Aug 2023: Two papers are accepted to RA-L.
  • Jan 2023: A paper is accepted to ICRA 2023.
  • Jun 2022: A paper is accepted to CVPR 2022 Workshop.
  • Education

    Ph.D. Candidate | Electronic and Computer Engineering (ECE), HKUST
    Time: 2021 - Present. Supervisor: Prof. Shaojie Shen


    B.Eng | Mechatronics Engineering (ME), HIT
    Time: 2017 - 2021.


    My research focuses on robotics and deep learning, including perception, prediction, and planning. Before HKUST, my research worked on perception and trajectory prediction in autonomous driving. In the present stage, I focus on autonomous and intelligent aerial inspection/reconstruction/exploration/coverage, and 3D scene understanding.


    (# for the corresponding author, * for equal contributions)

    MASSTAR: A Multi-Modal Large-Scale Scene Dataset with a Versatile Toolchain for Surface Prediction and Completion
    Jinqi Jiang*, Guiyong Zheng*, Chen Feng*, Shaojie Shen, and Boyu Zhou#.
    [Under Review] Submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024. Abu Dhabi, United Arab Emirates.

    We propose MASSTAR, a multi-modal large-scale scene dataset composed of over a thousand collected scene-level 3D data, which could be used for training and testing different learning methods. Additionally, a versatile and highly automatic toolchain is developed to generate a multi-modal dataset only from 3D model sets.


    Star-Searcher: A Complete and Efficient Aerial System for Autonomous Target Search in Complex Unknown Environments
    Yiming Luo, Zixuan Zhuang, Neng Pan, Chen Feng, Shaojie Shen, Fei Gao, Hui Cheng, and Boyu Zhou#.
    IEEE Robotics and Automation Letters (RA-L), 2024. Presented at IROS 2024, Abu Dhabi, United Arab Emirates.

    We propose Star-Searcher, a complete and efficient aerial system for autonomous target search in complex unknown environments. Our aerial system incorporates specialized sensor suites, mapping, and planning modules, all geared towards improving task efficiency and completeness.


    FC-Planner: A Skeleton-guided Planning Framework for Fast Aerial Coverage of Complex 3D Scenes
    Chen Feng, Haojia Li, Xinyi Chen, Shaojie Shen, and Boyu Zhou#.
    IEEE International Conference on Robotics and Automation (ICRA), 2024. Yokohama, Japan.

    We propose FC-Planner, a skeleton-guided planning framework tailored for fast coverage of large and complex 3D scenes, which results in the generation of high-quality coverage paths and high computational efficiency.


    MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction
    Chen Feng, Hangning Zhou, Huadong Lin, Zhigang Zhang, Ziyao Xu, Chi Zhang, Boyu Zhou#, and Shaojie Shen.
    IEEE Robotics and Automation Letters (RA-L), 2023. Presented at ICRA 2024, Yokohama, Japan.

    We propose MacFormer, an one-stage Map-Agent Coupled Transformer for real-time and robust trajectory prediction that explicitly incorporates map constraints into the network achieving state-of-the-art performance with significantly lower inference latency and fewer parameters.


    AutoTrans: A Complete Planning and Control Framework for Autonomous UAV Paylaod Transportation
    Haojia Li, Haokun Wang, Chen Feng, Fei Gao#, Boyu Zhou#, and Shaojie Shen.
    IEEE Robotics and Automation Letters (RA-L), 2023. Presented at ICRA 2024, Yokohama, Japan.

    We propose AutoTrans, a systematic solution for fully autonomous aerial payload transportation that includes a real-time planning solution to generate smooth trajectories and an adaptive NMPC with a hierarchical disturbance compensation strategy to overcome unknown external perturbations as well as inaccurate model parameters.


    PredRecon: A Prediction-boosted Planning Framework for Fast and High-quality Autonomous Aerial Reconstruction
    Chen Feng, Haojia Li, Fei Gao, Boyu Zhou#, and Shaojie Shen.
    IEEE International Conference on Robotics and Automation (ICRA), 2023. London, UK.

    We propose PredRecon, a prediction-boosted planning framework that can efficiently reconstruct high-quality 3D models for the target areas in unknown environments with a single flight.


    TENET: Transformer Encoding Network for Effective Temporal Flow on Motion Prediction
    Yuting Wang*, Hangning Zhou*,#, Zhigang Zhang*, Chen Feng, Huadong Lin, Chaofei Gao, Yizhi Tang, Zhenting Zhao, Shiyu Zhang, Jie Guo, Xuefeng Wang, Ziyao Xu, and Chi Zhang.
    IEEE / CVF Computer Vision and Pattern Recognition Conference Workshop on Autonomous Driving (CVPRW), 2022. New Orleans, USA.

    We propose TENET to enhance the trajectory temporal encoding via Temporal Flow Header. Besides, an efficient K-means ensemble method is used. Using our Transformer network and ensemble method, we win the first place of Argoverse 2 Motion Forecasting Challenge with the state-of-the-art brier-minFDE score of 1.90.


    Sep. 2021, Postgraduate Studentship - Hong Kong University of Science and Technology

    Jun. 2021, Outstanding Graduate - Harbin Institute of Technology

    Aug. 2020, ABU ROBOCON Robotics Competition First Prize - ABU

    Aug. 2019, National University Students Robotics Competition, RoboMaster First Prize - DJI

    Dec. 2018, Outstanding Student - Harbin Institute of Technology

    Dec. 2018, National Scholarship - Ministry of Education, PRC


    Reviewer: RA-L, ICRA, IROS, CVPR

    Teaching Assistant:

  • ELEC1100 Introduction to Electro-Robot Design (2023 Fall)
  • ELEC5660 Introduction to Aerial Robots (2024 Spring)
  • About Me

    Skills: Python / C++ / Matlab, PyTorch / MegEngine, Linux, ROS, OpenCV, SolidWorks, Adams, ANSYS, Mechanical design

    Last update: 2024.02.29. Thanks.