Biography

I am pursuing my M.S. degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My major research interest is multi-agent path finding.

Research and Interests

• Multi-Agent Path Finding

Publications

• Junhao Chen, Zhen Zhang, Chengrui Zhu, Xiaojun Hou, Tianyang Hu, Huifeng Wu, and Yong Liu. LITE: A Learning-Integrated Topological Explorer for Multi-Floor Indoor Environments. In 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025.
  [BibTeX] [Abstract] [DOI]

  This work focuses on multi-floor indoor exploration, which remains an open area of research. Compared to traditional methods, recent learning-based explorers have demonstrated significant potential due to their robust environmental learning and modeling capabilities, but most are restricted to 2D environments. In this paper, we proposed a learning-integrated topological explorer, LITE, for multi-floor indoor environments. LITE decomposes the environment into a floor-stair topology, enabling seamless integration of learning or non-learning-based 2D exploration methods for 3D exploration. As we incrementally build floor-stair topology in exploration using YOLO11-based instance segmentation model, the agent can transition between floors through a finite state machine. Additionally, we implement an attention-based 2D exploration policy that utilizes an attention mechanism to capture spatial dependencies between different regions, thereby determining the next global goal for more efficient exploration. Extensive comparison and ablation studies conducted on the HM3D and MP3D datasets demonstrate that our proposed 2D exploration policy significantly outperforms all baseline explorers in terms of exploration efficiency. Furthermore, experiments in several 3D multi-floor environments indicate that our framework is compatible with various 2D exploration methods, facilitating effective multi-floor indoor exploration. Finally, we validate our method in the real world with a quadruped robot, highlighting its strong generalization capabilities.

  @inproceedings{chen2025lite,
  title = {LITE: A Learning-Integrated Topological Explorer for Multi-Floor Indoor Environments},
  author = {Junhao Chen and Zhen Zhang and Chengrui Zhu and Xiaojun Hou and Tianyang Hu and Huifeng Wu and Yong Liu},
  year = 2025,
  booktitle = {2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  doi = {10.1109/IROS60139.2025.11246317},
  abstract = {This work focuses on multi-floor indoor exploration, which remains an open area of research. Compared to traditional methods, recent learning-based explorers have demonstrated significant potential due to their robust environmental learning and modeling capabilities, but most are restricted to 2D environments. In this paper, we proposed a learning-integrated topological explorer, LITE, for multi-floor indoor environments. LITE decomposes the environment into a floor-stair topology, enabling seamless integration of learning or non-learning-based 2D exploration methods for 3D exploration. As we incrementally build floor-stair topology in exploration using YOLO11-based instance segmentation model, the agent can transition between floors through a finite state machine. Additionally, we implement an attention-based 2D exploration policy that utilizes an attention mechanism to capture spatial dependencies between different regions, thereby determining the next global goal for more efficient exploration. Extensive comparison and ablation studies conducted on the HM3D and MP3D datasets demonstrate that our proposed 2D exploration policy significantly outperforms all baseline explorers in terms of exploration efficiency. Furthermore, experiments in several 3D multi-floor environments indicate that our framework is compatible with various 2D exploration methods, facilitating effective multi-floor indoor exploration. Finally, we validate our method in the real world with a quadruped robot, highlighting its strong generalization capabilities.}
  }

• Tianyang Hu, Zhen Zhang, Chengrui Zhu, Gang Xu, Yuchen Wu, Huifeng Wu, and Yong Liu. MARF: Cooperative Multi-Agent Path Finding with Reinforcement Learning and Frenet Lattice in Dynamic Environments. In 2025 IEEE International Conference on Robotics and Automation (ICRA), pages 12607-12613, 2025.
  [BibTeX] [Abstract] [DOI] [PDF]

  Multi-agent path finding (MAPF) in dynamic and complex environments is a highly challenging task. Recent research has focused on the scalability of agent numbers or the complexity of the environment. Usually, they disregard the agents’ physical constraints or use a differential-driven model. However, this approach fails to adequately capture the kinematic and dynamic constraints of real-world vehicles, particularly those equipped with Ackermann steering. This paper presents a novel algorithm named MARF that combines multi-agent reinforcement learning (MARL) with a Frenet lattice planner. The MARL foundation endows the algorithm with enhanced generalization capabilities while preserving computational efficiency. By incorporating Frenet lattice trajectories into the action space of the MARL framework, agents are capable of generating smooth and feasible trajectories that respect the kinematic and dynamic constraints. In addition, we adopt a centralized training and decentralized execution (CTDE) framework, where a network of shared value functions enables efficient cooperation among agents during decision-making. Simulation results and real-world experiments in different scenarios demonstrate that our method achieves superior performance in terms of success rate, average speed, extra distance of trajectory, and computing time.

  @inproceedings{hu2025marf,
  title = {MARF: Cooperative Multi-Agent Path Finding with Reinforcement Learning and Frenet Lattice in Dynamic Environments},
  author = {Tianyang Hu and Zhen Zhang and Chengrui Zhu and Gang Xu and Yuchen Wu and Huifeng Wu and Yong Liu},
  year = 2025,
  booktitle = {2025 IEEE International Conference on Robotics and Automation (ICRA)},
  pages = {12607-12613},
  doi = {10.1109/ICRA55743.2025.11128009},
  abstract = {Multi-agent path finding (MAPF) in dynamic and complex environments is a highly challenging task. Recent research has focused on the scalability of agent numbers or the complexity of the environment. Usually, they disregard the agents' physical constraints or use a differential-driven model. However, this approach fails to adequately capture the kinematic and dynamic constraints of real-world vehicles, particularly those equipped with Ackermann steering. This paper presents a novel algorithm named MARF that combines multi-agent reinforcement learning (MARL) with a Frenet lattice planner. The MARL foundation endows the algorithm with enhanced generalization capabilities while preserving computational efficiency. By incorporating Frenet lattice trajectories into the action space of the MARL framework, agents are capable of generating smooth and feasible trajectories that respect the kinematic and dynamic constraints. In addition, we adopt a centralized training and decentralized execution (CTDE) framework, where a network of shared value functions enables efficient cooperation among agents during decision-making. Simulation results and real-world experiments in different scenarios demonstrate that our method achieves superior performance in terms of success rate, average speed, extra distance of trajectory, and computing time.}
  }