Dongjie Yu
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Research
Publications

Publications

* means equal contributions.

Conference papers

  1. [WAFR’24] Dongjie Yu*, Hang Xu*, Yizhou Chen, Yi Ren and Jia Pan. “BiKC: Keypose-Conditioned Consistency Policy for Bimanual Robotic Manipulatio”. In The 16th International Workshop on the Algorithmic Foundations of Robotics, 2024.

    1. tldr: We present Bimanual Keypose-Conditioned Consistency Policy (BiKC) to enhance the operational reliability and efficiency by overcoming per-stage error and per-step latency accumulated across multiple stages.

    2. [Paper] [Code]

  2. [ICML’22] Dongjie Yu*, Haitong Ma*, Shengbo Eben Li and Jianyu Chen. “Reachability Constrained Reinforcement Learning”. In Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25636-25655, 2022.

    1. tldr: We characterize feasible sets (persistently safe states) with Hamilton-Jacobi reachability analysis in constrained RL. The algorithm reachs a zero-violation policy with competitive performance.

    2. [Paper] [Code_learning] [Code_env]

Journal papers

  1. [T-ASE] Hang Xu, Yizhou Chen, Dongjie Yu, Yi Ren and Jia Pan. “BiKC+: Bimanual Hierarchical Imitation with Keypose-Conditioned Coordination-Aware Consistency Policies”. IEEE Transactions on Automation Science and Engineering, DOI: 10.1109/TASE.2025.3639130, 2025.

    1. tldr: We use a VLM to autonomously identify contact mode to optionally merge bimanual keyposes, thus enabling coordination-aware bimanual manipulation.

    2. [Paper] [Code]

  2. [T-ASE] Dongjie Yu, Wenjun Zou, Yujie Yang, Haitong Ma, Shengbo Eben Li, Yuming Yin, Jianyu Chen and Jingliang Duan. “Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability Certificate”. IEEE Transactions on Automation Science and Engineering, DOI: 10.1109/TASE.2023.3292388, 2023.

    1. tldr: We propose a distributional form of reachability certificate to consider the uncertaity of the system dynamics in safe RL. The algorithm is able to learn a zero-violation policy with competitive performance, significantly reducing violations during exploration.

    2. [Paper] [Code]

Preprints

  1. Kun Lei*, Huanyu Li*, Dongjie Yu*, Zhenyu Wei, Lingxiao Guo, Zhennan Jiang, Ziyu Wang, Shiyu Liang and Huazhe Xu. “RL-100: Performant Robotic Manipulation with Real-World Reinforcement Learning”, arXiv preprint 2510.14830, 2025.

    1. tldr: We introduce RL-100, a real-world RL system consisting of imitation, iterative offline and online RL. RL-100 achieves 100% success rate on eight diverse real-world tasks, demonstrating the potential of real-world RL for deployable robotic manipulation.

    2. [Paper]