Biography

I received my B.S. degree from Zhejiang University of Technology in 2018. I am currently pursuing the Ph.D. degree with the institute of Cyber Systems and Control, Zhejiang University, Hangzhou, China, working with Prof. Y. Liu. My research area includes autonomous navigation of mobile robots, motion planning of quadruped robots, exploration planning, active-SLAM, multi-agent collaboration and so on.

Research and Interests

• Autonomous Navigation
• Motion Planning
• Exploration planning
• Active-SLAM

Publications

• Chengrui Zhu, Zhen Zhang, Weiwei Liu, Siqi Li, and Yong Liu. Learning Accurate and Robust Velocity Tracking for Quadrupedal Robots. Journal of Field Robotics, 2025.
  [BibTeX] [DOI]

  @article{zhu2025lar,
  title = {Learning Accurate and Robust Velocity Tracking for Quadrupedal Robots},
  author = {Chengrui Zhu and Zhen Zhang and Weiwei Liu and Siqi Li and Yong Liu},
  year = 2025,
  journal = {Journal of Field Robotics},
  doi = {10.1002/rob.70028}
  }

• Dianyong Hou, Chengrui Zhu, Zhen Zhang, Zhibin Li, Chuang Guo, and Yong Liu. Efficient Learning of A Unified Policy For Whole-body Manipulation and Locomotion Skills. In 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025.
  [BibTeX] [Abstract] [DOI]

  Equipping quadruped robots with manipulators provides unique loco-manipulation capabilities, enabling diverse practical applications. This integration creates a more complex system that has increased difficulties in modeling and control. Reinforcement learning (RL) offers a promising solution to address these challenges by learning optimal control policies through interaction. Nevertheless, RL methods often struggle with local optima when exploring large solution spaces for motion and manipulation tasks. To overcome these limitations, we propose a novel approach that integrates an explicit kinematic model of the manipulator into the RL framework. This integration provides feedback on the mapping of the body postures to the manipulator’s workspace, guiding the RL exploration process and effectively mitigating the local optima issue. Our algorithm has been successfully deployed on a DeepRobotics X20 quadruped robot equipped with a Unitree Z1 manipulator, and extensive experimental results demonstrate the superior performance of this approach. We have established a project website to showcase our experiments.

  @inproceedings{hou2025elo,
  title = {Efficient Learning of A Unified Policy For Whole-body Manipulation and Locomotion Skills},
  author = {Dianyong Hou and Chengrui Zhu and Zhen Zhang and Zhibin Li and Chuang Guo and Yong Liu},
  year = 2025,
  booktitle = {2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  doi = {10.1109/IROS60139.2025.11246644},
  abstract = {Equipping quadruped robots with manipulators provides unique loco-manipulation capabilities, enabling diverse practical applications. This integration creates a more complex system that has increased difficulties in modeling and control. Reinforcement learning (RL) offers a promising solution to address these challenges by learning optimal control policies through interaction. Nevertheless, RL methods often struggle with local optima when exploring large solution spaces for motion and manipulation tasks. To overcome these limitations, we propose a novel approach that integrates an explicit kinematic model of the manipulator into the RL framework. This integration provides feedback on the mapping of the body postures to the manipulator’s workspace, guiding the RL exploration process and effectively mitigating the local optima issue. Our algorithm has been successfully deployed on a DeepRobotics X20 quadruped robot equipped with a Unitree Z1 manipulator, and extensive experimental results demonstrate the superior performance of this approach. We have established a project website to showcase our experiments.}
  }

• Chengrui Zhu, Zhen Zhang, Siqi Li, Qingpeng Li, and Yong Liu. Learning Symmetric Legged Locomotion via State Distribution Symmetrization. In 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025.
  [BibTeX] [Abstract] [DOI]

  Morphological symmetry is a fundamental characteristic of legged animals and robots. Most existing Deep Reinforcement Learning approaches for legged locomotion neglect to exploit this inherent symmetry, often producing unnatural and suboptimal behaviors such as dominant legs or non-periodic gaits. To address this limitation, we propose a novel learning-based framework to systematically optimize symmetry by state distribution symmetrization. First, we introduce the degree of asymmetry (DoA), a quantitative metric that measures the discrepancy between original and mirrored state distributions. Second, we develop an efficient computation method for DoA using gradient ascent with a trained discriminator network. This metric is then incorporated into a reinforcement learning framework by introducing it to the reward function, explicitly encouraging symmetry during policy training. We validate our framework with extensive experiments on quadrupedal and humanoid robots in simulated and real-world environments. Results demonstrate the efficacy of our approach for improving policy symmetry and overall locomotion performance.

  @inproceedings{zhu2025lsl,
  title = {Learning Symmetric Legged Locomotion via State Distribution Symmetrization},
  author = {Chengrui Zhu and Zhen Zhang and Siqi Li and Qingpeng Li and Yong Liu},
  year = 2025,
  booktitle = {2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  doi = {10.1109/IROS60139.2025.11246183},
  abstract = {Morphological symmetry is a fundamental characteristic of legged animals and robots. Most existing Deep Reinforcement Learning approaches for legged locomotion neglect to exploit this inherent symmetry, often producing unnatural and suboptimal behaviors such as dominant legs or non-periodic gaits. To address this limitation, we propose a novel learning-based framework to systematically optimize symmetry by state distribution symmetrization. First, we introduce the degree of asymmetry (DoA), a quantitative metric that measures the discrepancy between original and mirrored state distributions. Second, we develop an efficient computation method for DoA using gradient ascent with a trained discriminator network. This metric is then incorporated into a reinforcement learning framework by introducing it to the reward function, explicitly encouraging symmetry during policy training. We validate our framework with extensive experiments on quadrupedal and humanoid robots in simulated and real-world environments. Results demonstrate the efficacy of our approach for improving policy symmetry and overall locomotion performance.}
  }

• 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.}
  }

• Deye Zhu, Chengrui Zhu, Zhen Zhang, Shuo Xin, and Yong Liu. Learning Safe Locomotion for Quadrupedal Robots by Derived-Action Optimization. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 6870-6876, 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  Deep reinforcement learning controllers with exteroception have enabled quadrupedal robots to traverse terrain robustly. However, most of these controllers heavily depend on complex reward functions and suffer from poor convergence. This work proposes a novel learning framework called derived-action optimization. The derived action is defined as a high-level representation of a policy and can be introduced into the reward function to guide decision-making behaviors. The proposed derived-action optimization method is applied to learn safer quadrupedal locomotion, achieving fast convergence and better performance. Specifically, we choose the foothold as the derived action and optimize the flatness of the terrain around the foothold to reduce potential sliding and collisions. Extensive experiments demonstrate the high safety and effectiveness of our method.

  @inproceedings{zhu2024lsl,
  title = {Learning Safe Locomotion for Quadrupedal Robots by Derived-Action Optimization},
  author = {Deye Zhu and Chengrui Zhu and Zhen Zhang and Shuo Xin and Yong Liu},
  year = 2024,
  booktitle = {2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages = {6870-6876},
  doi = {10.1109/IROS58592.2024.10802725},
  abstract = {Deep reinforcement learning controllers with exteroception have enabled quadrupedal robots to traverse terrain robustly. However, most of these controllers heavily depend on complex reward functions and suffer from poor convergence. This work proposes a novel learning framework called derived-action optimization. The derived action is defined as a high-level representation of a policy and can be introduced into the reward function to guide decision-making behaviors. The proposed derived-action optimization method is applied to learn safer quadrupedal locomotion, achieving fast convergence and better performance. Specifically, we choose the foothold as the derived action and optimize the flatness of the terrain around the foothold to reduce potential sliding and collisions. Extensive experiments demonstrate the high safety and effectiveness of our method.}
  }

• Shuo Xin, Zhen Zhang, Liang Liu, Xiaojun Hou, Deye Zhu, Mengmeng Wang, and Yong Liu. A Robotic-centric Paradigm for 3D Human Tracking Under Complex Environments Using Multi-modal Adaptation. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4934-4940, 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  The goal of this paper is to strike a feasible tracking paradigm that can make 3D human trackers applicable on robot platforms and enable more high-level tasks. Till now, two fundamental problems haven’t been adequately addressed. One is the computational cost lightweight enough for robotic deployment, and the other is the easily-influenced accuracy varied greatly in complex real environments. In this paper, a robotic-centric tracking paradigm called MATNet is proposed that directly matches the LiDAR point clouds and RGB videos through end-to-end learning. To improve the low accuracy of human tracking against disturbance, a coarse-to-fine Transformer along with target-ware augmentation is proposed by fusing RGB videos and point clouds through a pyramid encoding and decoding strategy. To better meet the real-time requirement of actual robot deployment, we introduce the parameter-efficient adaptation tuning that greatly shortens the model’s training time. Furthermore, we also propose a five-step Anti-shake Refinement strategy and have added human prior values to overcome the strong shaking on the robot plat-form. Extensive experiments confirm that MATNet significantly outperforms the previous state-of-the-art on both open-source datasets and large-scale robotic datasets.

  @inproceedings{xin2024arc,
  title = {A Robotic-centric Paradigm for 3D Human Tracking Under Complex Environments Using Multi-modal Adaptation},
  author = {Shuo Xin and Zhen Zhang and Liang Liu and Xiaojun Hou and Deye Zhu and Mengmeng Wang and Yong Liu},
  year = 2024,
  booktitle = {2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages = {4934-4940},
  doi = {10.1109/IROS58592.2024.10802166},
  abstract = {The goal of this paper is to strike a feasible tracking paradigm that can make 3D human trackers applicable on robot platforms and enable more high-level tasks. Till now, two fundamental problems haven't been adequately addressed. One is the computational cost lightweight enough for robotic deployment, and the other is the easily-influenced accuracy varied greatly in complex real environments. In this paper, a robotic-centric tracking paradigm called MATNet is proposed that directly matches the LiDAR point clouds and RGB videos through end-to-end learning. To improve the low accuracy of human tracking against disturbance, a coarse-to-fine Transformer along with target-ware augmentation is proposed by fusing RGB videos and point clouds through a pyramid encoding and decoding strategy. To better meet the real-time requirement of actual robot deployment, we introduce the parameter-efficient adaptation tuning that greatly shortens the model's training time. Furthermore, we also propose a five-step Anti-shake Refinement strategy and have added human prior values to overcome the strong shaking on the robot plat-form. Extensive experiments confirm that MATNet significantly outperforms the previous state-of-the-art on both open-source datasets and large-scale robotic datasets.}
  }

• Shuo Xin, Zhen Zhang, Mengmeng Wang, Xiaojun Hou, Yaowei Guo, Xiao Kang, Liang Liu, and Yong Liu. Multi-modal 3D Human Tracking for Robots in Complex Environment with Siamese Point-Video Transformer. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 337-344, 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  Tracking a specific person in 3D scene is gaining momentum due to its numerous applications in robotics. Currently, most 3D trackers focus on driving scenarios with neglected jitter and uncomplicated surroundings, which results in their severe degeneration in complex environments, especially on jolting robot platforms (only 20-60% success rate). To improve the accuracy, a Point-Video-based Transformer Tracking model (PVTrack) is presented for robots. It is the first multi-modal 3D human tracking work that incorporates point clouds together with RGB videos to achieve information complementarity. Moreover, PVTrack proposes the Siamese Point-Video Transformer for feature aggregation to overcome dynamic environments, which captures more target-aware information through the hierarchical attention mechanism adaptively. Considering the violent shaking on robots and rugged terrains, a lateral Human-ware Proposal Network is designed together with an Anti-shake Proposal Compensation module. It alleviates the disturbance caused by complex scenes as well as the particularity of the robot platform. Experiments show that our method achieves state-of-the-art performance on both KITTI/Waymo datasets and a quadruped robot for various indoor and outdoor scenes.

  @inproceedings{xin2024mmh,
  title = {Multi-modal 3D Human Tracking for Robots in Complex Environment with Siamese Point-Video Transformer},
  author = {Shuo Xin and Zhen Zhang and Mengmeng Wang and Xiaojun Hou and Yaowei Guo and Xiao Kang and Liang Liu and Yong Liu},
  year = 2024,
  booktitle = {2024 IEEE International Conference on Robotics and Automation (ICRA)},
  pages = {337-344},
  doi = {10.1109/ICRA57147.2024.10610979},
  abstract = {Tracking a specific person in 3D scene is gaining momentum due to its numerous applications in robotics. Currently, most 3D trackers focus on driving scenarios with neglected jitter and uncomplicated surroundings, which results in their severe degeneration in complex environments, especially on jolting robot platforms (only 20-60% success rate). To improve the accuracy, a Point-Video-based Transformer Tracking model (PVTrack) is presented for robots. It is the first multi-modal 3D human tracking work that incorporates point clouds together with RGB videos to achieve information complementarity. Moreover, PVTrack proposes the Siamese Point-Video Transformer for feature aggregation to overcome dynamic environments, which captures more target-aware information through the hierarchical attention mechanism adaptively. Considering the violent shaking on robots and rugged terrains, a lateral Human-ware Proposal Network is designed together with an Anti-shake Proposal Compensation module. It alleviates the disturbance caused by complex scenes as well as the particularity of the robot platform. Experiments show that our method achieves state-of-the-art performance on both KITTI/Waymo datasets and a quadruped robot for various indoor and outdoor scenes.}
  }

• Shuo Xin, Liang Liu, Xiao Kang, Zhen Zhang, Mengmeng Wang, and Yong Liu. Beyond Traditional Driving Scenes: A Robotic-Centric Paradigm for 2D+3D Human Tracking Using Siamese Transformer Network. In 7th International Symposium on Autonomous Systems (ISAS), 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  3D human tracking plays a crucial role in the automation intelligence system. Current approaches focus on achieving higher performance on traditional driving datasets like KITTI, which overlook the jitteriness of the platform and the complexity of the environments. Once the scenarios are migrated to jolting robot platforms, they all degenerate severely with only a 20-60% success rate, which greatly restricts the high-level application of autonomous systems. In this work, beyond traditional flat scenes, we introduce Multi-modal Human Tracking Paradigm (MHTrack), a unified multimodal transformer-based model that can effectively track the target person frame-by-frame in point and video sequences. Specifically, we design a speed-inertia module-assisted stabilization mechanism along with an alternate training strategy to better migrate the tracking algorithm to the robot platform. To capture more target-aware information, we combine the geometric and appearance features of point clouds and video frames together based on a hierarchical Siamese Transformer Network. Additionally, considering the prior characteristics of the human category, we design a lateral cross-attention pyramid head for deeper feature aggregation and final 3D BBox generation. Extensive experiments confirm that MHTrack significantly outperforms the previous state-of-the-arts on both open-source datasets and large-scale robotic datasets. Further analysis verifies each component’s effectiveness and shows the robotic-centric paradigm’s promising potential when deployed into dynamic robotic systems.

  @inproceedings{xin2024btd,
  title = {Beyond Traditional Driving Scenes: A Robotic-Centric Paradigm for 2D+3D Human Tracking Using Siamese Transformer Network},
  author = {Shuo Xin and Liang Liu and Xiao Kang and Zhen Zhang and Mengmeng Wang and Yong Liu},
  year = 2024,
  booktitle = {7th International Symposium on Autonomous Systems (ISAS)},
  doi = {10.1109/ISAS61044.2024.10552604},
  abstract = {3D human tracking plays a crucial role in the automation intelligence system. Current approaches focus on achieving higher performance on traditional driving datasets like KITTI, which overlook the jitteriness of the platform and the complexity of the environments. Once the scenarios are migrated to jolting robot platforms, they all degenerate severely with only a 20-60% success rate, which greatly restricts the high-level application of autonomous systems. In this work, beyond traditional flat scenes, we introduce Multi-modal Human Tracking Paradigm (MHTrack), a unified multimodal transformer-based model that can effectively track the target person frame-by-frame in point and video sequences. Specifically, we design a speed-inertia module-assisted stabilization mechanism along with an alternate training strategy to better migrate the tracking algorithm to the robot platform. To capture more target-aware information, we combine the geometric and appearance features of point clouds and video frames together based on a hierarchical Siamese Transformer Network. Additionally, considering the prior characteristics of the human category, we design a lateral cross-attention pyramid head for deeper feature aggregation and final 3D BBox generation. Extensive experiments confirm that MHTrack significantly outperforms the previous state-of-the-arts on both open-source datasets and large-scale robotic datasets. Further analysis verifies each component's effectiveness and shows the robotic-centric paradigm's promising potential when deployed into dynamic robotic systems.}
  }

• Zhen Zhang, Jiaqing Yan, Xin Kong, Guangyao Zhai, and Yong Liu. Efficient Motion Planning based on Kinodynamic Model for Quadruped Robots Following Persons in Confined Spaces. IEEE/ASME Transactions on Mechatronics, 26:1997-2006, 2021.
  [BibTeX] [Abstract] [DOI] [PDF]

  Quadruped robots have superior terrain adaptability and flexible movement capabilities than traditional robots. In this paper, we innovatively apply it in person-following tasks, and propose an efficient motion planning scheme for quadruped robots to generate a flexible and effective trajectory in confined spaces. The method builds a real-time local costmap via onboard sensors, which involves both static and dynamic obstacles. And we exploit a simplified kinodynamic model and formulate the friction pyramids formed by Ground Reaction Forces (GRFs) inequality constraints to ensure the executable of the optimized trajectory. In addition, we obtain the optimal following trajectory in the costmap completely based on the robots rectangular footprint description, which ensures that it can walk through the narrow spaces avoiding collision. Finally, a receding horizon control strategy is employed to improve the robustness of motion in complex environments. The proposed motion planning framework is integrated on the quadruped robot JueYing and tested in simulation as well as real scenarios. It shows that the execution success rates in various scenes are all over 90\%.

  @article{zhang2021emp,
  title = {Efficient Motion Planning based on Kinodynamic Model for Quadruped Robots Following Persons in Confined Spaces},
  author = {Zhen Zhang and Jiaqing Yan and Xin Kong and Guangyao Zhai and Yong Liu},
  year = 2021,
  journal = {IEEE/ASME Transactions on Mechatronics},
  volume = 26,
  pages = {1997-2006},
  doi = {10.1109/TMECH.2021.3083594},
  abstract = {Quadruped robots have superior terrain adaptability and flexible movement capabilities than traditional robots. In this paper, we innovatively apply it in person-following tasks, and propose an efficient motion planning scheme for quadruped robots to generate a flexible and effective trajectory in confined spaces. The method builds a real-time local costmap via onboard sensors, which involves both static and dynamic obstacles. And we exploit a simplified kinodynamic model and formulate the friction pyramids formed by Ground Reaction Forces (GRFs) inequality constraints to ensure the executable of the optimized trajectory. In addition, we obtain the optimal following trajectory in the costmap completely based on the robots rectangular footprint description, which ensures that it can walk through the narrow spaces avoiding collision. Finally, a receding horizon control strategy is employed to improve the robustness of motion in complex environments. The proposed motion planning framework is integrated on the quadruped robot JueYing and tested in simulation as well as real scenarios. It shows that the execution success rates in various scenes are all over 90\%.}
  }

• Guangyao Zhai, Zhen Zhang, Xin Kong, and Yong Liu. Efficient Pedestrian Following by Quadruped Robots. In 2021 IEEE International Conference on Robotics and Automation Workshop, 2021.
  [BibTeX] [Abstract] [PDF]

  Legged robots have superior terrain adaptability and flexible movement capabilities than traditional wheeled robots. In this work, we use a quadruped robot as an example of legged robots to complete a pedestrian-following task in challenging scenarios. The whole system consists of two modules: the perception and planning module, relying on the various onboard sensors.

  @inproceedings{zhai2021epf,
  title = {Efficient Pedestrian Following by Quadruped Robots},
  author = {Guangyao Zhai and Zhen Zhang and Xin Kong and Yong Liu},
  year = 2021,
  booktitle = {2021 IEEE International Conference on Robotics and Automation Workshop},
  abstract = {Legged robots have superior terrain adaptability and flexible movement capabilities than traditional wheeled robots. In this work, we use a quadruped robot as an example of legged robots to complete a pedestrian-following task in challenging scenarios. The whole system consists of two modules: the perception and planning module, relying on the various onboard sensors.}
  }