Publications

Selected recently published articles. Please refer to my Google Scholar for the full list.

2026

  1. CVPR
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    Pvp: Data-efficient humanoid robot learning with proprioceptive-privileged contrastive representations
    Mingqi Yuan, Tao Yu, Haolin Song, Bo Li, Xin Jin, and 2 more authors
    In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026
  2. CVPR
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    Goal-Driven Reward by Video Diffusion Models for Reinforcement Learning
    Qi Wang, Mian Wu, Yuyang Zhang, Mingqi Yuan, Wenyao Zhang, and 5 more authors
    In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026
  3. IROS
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    Atomvla: Scalable post-training for robotic manipulation via predictive latent world models
    Xiaoquan Sun, Zetian Xu, Chen Cao, Zonghe Liu, Yihan Sun, and 7 more authors
    In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2026
    Corresponding author

2025

  1. TPAMI
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    A survey of behavior foundation model: Next-generation whole-body control system of humanoid robots
    Mingqi Yuan, Tao Yu, Wenqi Ge, Xiuyong Yao, Dapeng Li, and 6 more authors
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025
  2. TMLR
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    RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning
    Mingqi Yuan, Roger Creus Castanyer, Bo Li, Xin Jin, Glen Berseth, and 1 more author
    Transactions on Machine Learning Research, 2025
  3. RA-L
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    Gait-Adaptive Perceptive Humanoid Locomotion with Real-Time Under-Base Terrain Reconstruction
    Haolin Song, Hongbo Zhu, Tao Yu, Yan Liu, Mingqi Yuan, and 3 more authors
    IEEE Robotics and Automation Letters (RA-L), 2025
  4. ICCV
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    Ultho: Ultra-lightweight yet efficient hyperparameter optimization in deep reinforcement learning
    Mingqi Yuan, Bo Li, Xin Jin, and Wenjun Zeng
    In IEEE/CVF International Conference on Computer Vision (ICCV), 2025

2023

  1. ICML
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    Automatic intrinsic reward shaping for exploration in deep reinforcement learning
    Mingqi Yuan, Bo Li, Xin Jin, and Wenjun Zeng
    In International Conference on Machine Learning (ICML), 2023