Yang Xu

Brief bio

I am currently a Postdoctoral Research Fellow supervised by Prof. Jiming Chen at Zhejiang University, China. I received my D. Eng. degree at Zhejiang University (2023) under the supervision of Prof. Meiqin Liu and Prof. Ronghao Zheng. I was also a visiting Ph.D. student with the Robotics Institute, University of Technology Sydney (UTS) supervised by Prof. Shoudong Huang and Dr. Liang Zhao.

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Research interests

My research interests include active SLAM, autonomous exploration, informative path planning, and robot learning in robotics. My current research projects mainly focus on learning-based robot exploration in hazardous and complex environments using ground robots. Welcome to reach out via email: garryhsu0901@gmail.com(Permanent)/xuyang94@zju.edu.cn (Academic).

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Academic services

Editorial Services: ICRA 2025, Associate Editor; RO-MAN 2024/2025, Associate Editor.

Conference Services: ICRA 2024, Session Co-Chair (SLAM); ICUS 2024, PC Member/Invited Session Chair.

Membership: IEEE, IEEE Robotics and Automation Society (RAS), CAA.

Journal Reviewer: IEEE Trans. Autom. Sci. Eng., IEEE Robot. Autom. Lett., IEEE Trans. Cogn. Develop. Syst., IEEE Trans. Intell. Transport. Syst., IEEE Trans. Intell. Vehicle., IEEE Trans. Instrum. Meas., IEEE Trans. Indus. Inform., IEEE/CAA Journal of Autom. Sin., Robot. Auton. Syst., Meas. Sci. Tech., IEEE Sensor J.

Conference Reviewer: ICRA, IROS, AIM, ICONIP(International Conference on Neural Information Processing), CCC(China Control Conference), CCDM(China Conference on Data Mining)

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Awards

• National Postdoctoral Program for Innovative Talents (10 in automation nationwide), 2024
• Chinese National Scholarship for Ph.D. Students (2%), Oct. 2022
• Chinese Scholarship Council for Joint Ph.D. Students, Jul. 2022
• Academic Rising Stars of Ph.D. Students, Zhejiang University, Aug. 2022
• Outstanding Graduate Student of Zhejiang University, Oct. 2022

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Talks and presentations

• ICRA 2024 (Yokohama, Japan) - CARE: Confidence-rich autonomous robot exploration using Bayesian kernel inference and optimization. Video

• 2024-04-27: Online talk & session chair at PRE-ICRA 2024. (Invited by CAA TC on Hybrid Intelligence)

• 2023-05-18: On-site talk at MiLab of Westlake University. (Invited by Prof. Donglin Wang)~

• 2022-10-15: Online talk at PRE-IROS 2022. (Invited by CAA TC on Hybrid Intelligence)

• IROS 2022 (Kyoto, Japan) - Confidence-rich localization and mapping based on particle filter for robotic exploration. Video

• IROS 2021 (Prague, Czech) - CRMI: Confidence-rich mutual information for information-theoretic mapping, Video here: Youtube, Bilibili

Posts and blogs

See all posts here: Posts

Selected publications

Find out my full publication list via Google Scholar.
^* co-first author with equal contribution; ^# Corresponding author.

1. 

   DEFORM: Adaptive Formation Reconfiguration of Multi-robot Systems in Confined Environments Jin Li*, Yang Xu*, Xiufang Shi, and Liang Li^# IEEE Robotics and Automation Letters 2025 [Abs] [Video] [Webpage] [Code]

   Achieving desired formation patterns without collisions is rather challenging for multi-robot systems in unknown obstacle-rich and confined environments, especially in narrow corridor scenes containing large-volume obstacles. To address this, we propose an adaptive formation reconfiguration method that can dynamically switch to the optimal formation pattern based on the current obstacle distribution. Specifically, we develop a novel obstacle-free maximum passable width detection method to formulate recursive optimization problems, which can determine the currently best formation shape and refine local goals away from obstacles. Then we design a task assignment module for the temporary leader robot and a consensus-based distributed formation controller for each robot using model predictive control to ensure rapid convergence to the suggested formation shape. In addition, we utilize the potential field approach for each robot to improve collision avoidance. Extensive Gazebo simulations and real-world experiments in confined and obstacle-rich scenes verify the superior performance of our methods compared to cutting-edge methods.

2. 

   TaskExp: Enhancing Generalization of Multi-Robot Exploration with Multi-Task Pre-Training Shaohao Zhu, Yixian Zhao, Yang Xu, Anjun Chen, Jiming Chen, and Jinming Xu^#
   2025 IEEE International Conference on Robotics and Automation (ICRA) 2025 [Abs]

   We aim to develop a general multi-agent reinforcement learning (MARL) policy that enables a group of robots to efficiently explore large-scale, unknown environments with random pose initialization. Existing MARL-based multirobot exploration methods face challenges in reliably mapping observations to actions in large-scale scenarios and lack of zero-shot generalization to unknown environments. To this end, we propose a generic multi-task pre-training algorithm (termed TaskExp) to enhance the generalization of learningbased policies. In particular, we design a decision-related task to guide the policy to focus on valuable subspaces of the action space, improving the reliability of policy mapping. Moreover, two perception-related tasks–Location Estimation and Map Prediction–are designed to enhance the zero-shot capability of the policy by guiding it to extract general invariant features from unknown environments. With TaskExp pre-training, our policy significantly outperforms state-of-the-art planningbased methods in large-scale scenarios and demonstrates strong zero-shot performance in unseen environments. Furthermore, TaskExp can also be easily integrated to improve the existing learning-based multi-robot exploration methods.

3. 

   AF-RLIO: Adaptive Fusion of Radar-LiDAR-Inertial Information for Robust Odometry in Challenging Environments Chenglong Qian*, Yang Xu*, Xiufang Shi, Jiming Chen, and Liang Li^#
   2025 IEEE International Conference on Robotics and Automation (ICRA) 2025 [Abs]

   In robotic navigation, maintaining precise pose estimation and navigation in complex and dynamic environments is crucial. However, environmental challenges such as smoke, tunnels, and adverse weather can significantly degrade the performance of single-sensor systems like LiDAR or GPS, compromising the overall stability and safety of autonomous robots. To address these challenges, we propose AF-RLIO: an adaptive fusion approach that integrates 4D millimeterwave radar, LiDAR, inertial measurement unit (IMU), and GPS to leverage the complementary strengths of these sensors for robust odometry estimation in complex environments. Our method consists of three key modules. Firstly, the pre-processing module utilizes radar data to assist LiDAR in removing dynamic points and determining when environmental conditions are degraded for LiDAR. Secondly, the front-end odometry selects appropriate point cloud data for scan-to-submap matching and tightly couples it with the IMU using the Iterative Error State Kalman Filter. Lastly, the back-end optimization module balances weights between front-end odometry and GPS data, constructing a pose graph for optimization. The proposed approach has been evaluated on datasets and tested in realworld robotic environments, demonstrating its effectiveness and advantages over existing methods in challenging conditions such as smoke and tunnels.

4. 

   Multi-robot autonomous 3D reconstruction using Gaussian splatting with Semantic guidance Jing Zeng, Qi Ye^#, Tianle Liu, Yang Xu, Jin Li, Jinming Xu, Liang Li, and Jiming Chen IEEE Robotics and Automation Letters 2025 [Abs]

   Implicit neural representations and 3D Gaussian splatting (3DGS) have shown great potential for scene reconstruction. Recent studies have expanded their applications in autonomous reconstruction through task assignment methods. However, these methods are mainly limited to single robot, and rapid reconstruction of large-scale scenes remains challenging. Additionally, task-driven planning based on surface uncertainty is prone to being trapped in local optima. To this end, we propose the first 3DGS-based centralized multi-robot autonomous 3D reconstruction framework. To further reduce time cost of task generation and improve reconstruction quality, we integrate online open-vocabulary semantic segmentation with surface uncertainty of 3DGS, focusing view sampling on regions with high instance uncertainty. Finally, we develop a multi-robot collaboration strategy with mode and task assignments improving reconstruction quality while ensuring planning efficiency. Our method demonstrates the highest reconstruction quality among all planning methods and superior planning efficiency compared to existing multi-robot methods. We deploy our method on multiple robots, and results show that it can effectively plan view paths and reconstruct scenes with high quality.

1. 

   Uncertainty-Aware Autonomous Robot Exploration Using Confidence-Rich Localization and Mapping Yang Xu, Ronghao Zheng^#, Senlin Zhang, Meiqin Liu, and Junzhi Yu IEEE Transactions on Automation Science and Engineering 2024 [Abs] [Webpage]

   Information-based autonomous robot exploration methods, aiming to maximize the exploration rewards, e.g., mutual information (MI), get more prevalent in field robotics applications. However, most MI-based exploration methods assume known poses or use inaccurate pose uncertainty approximation, which may lead to deviation or even failure when exploring prior unknown environments. In this paper, we explicitly consider full-state (pose & map) uncertainty for balancing exploration and localizability, i.e., avoiding the robot guiding itself to complex scenes with high exploration rewards but hard to localize. We first propose a Rao-Blackwellized particle filter-based localization and mapping framework (RBPF-CLAM) for a dense environmental map with continuous occupancy distribution. Then we develop a new closed-form particle weighting method to improve the localization accuracy and robustness. We further use these weighted particles to approximate the unknown pose uncertainty and combine it with our previous confidence-rich mutual information (CRMI) metric to evaluate the expected information utility of the robot’s new control actions. This new information metric is called uncertain CRMI (UCRMI). Dataset experiments show our RBPF-CLAM improves about 44.7% average root mean square error than the state-of-the-art RBPF localization method, and real-world experimental results show that our UCRMI reduces the pose uncertainty about 32.85% more than CRMI and 25.36% time cost than UGPVR in the exploration of unknown and unstructured scenes given sparse measurements, which shows better performance than other state-of-the-art information metrics.

2. 

   Online Informative Path Planning of Autonomous Vehicles Using Kernel-Based Bayesian Optimization Yang Xu, Ronghao Zheng^#, Senlin Zhang, Meiqin Liu, and Junzhi Yu IEEE Transactions on Circuits and Systems II: Express Briefs 2024 [Abs] [Webpage]

   To improve environmental information gathering of intelligent vehicles in unknown scenes, this brief presents a hierarchical online informative path planning (IPP) framework containing global action optimization and local path planning. Particularly, we propose a lightweight kernel-based Bayesian optimization for IPP (KBO-IPP) to facilitate highly efficient information utility evaluation and decision-making of control actions. Specifically, KBO-IPP can infer the exact environmental mutual information (MI) and associated uncertainties with an approximate logarithmic complexity, eliminating the need for explicit model training. We develop a new information-theoretic objective function consisting of travel cost and predicted MI values with uncertainties to achieve the balance between high MI values (exploitation) and high prediction variances (exploration). To enhance the optimality of IPP, the past unselected informative actions are also incorporated into the global Bayesian optimization. Online real-world experiments validate that our proposed method shows higher efficiency with comparable performance to modern methods in unknown, complex environments.

1. 

   CARE: Confidence-rich Autonomous Robot Exploration using Bayesian Kernel Inference and Optimization Yang Xu, Ronghao Zheng^#, Senlin Zhang, Meiqin Liu, and Shoudong Huang IEEE Robotics and Automation Letters (presented at ICRA 2024) 2023 [Abs] [arXiv] [Webpage] [Supp] [Code]

   In this letter, we consider improving the efficiency of information-based autonomous robot exploration in unknown and complex environments. We first utilize Gaussian process (GP) regression to learn a surrogate model to infer the confidence-rich mutual information (CRMI) of querying control actions, then adopt an objective function consisting of predicted CRMI values and prediction uncertainties to conduct Bayesian optimization (BO), i.e., GP-based BO (GPBO). The trade-off between the best action with the highest CRMI value (exploitation) and the action with high prediction variance (exploration) can be realized. To further improve the efficiency of GPBO, we propose a novel lightweight information gain inference method based on Bayesian kernel inference and optimization (BKIO), achieving an approximate logarithmic complexity without the need for training. BKIO can also infer the CRMI and generate the best action using BO with bounded cumulative regret, which ensures its comparable accuracy to GPBO with much higher efficiency. Extensive numerical and real-world experiments show the desired efficiency of our proposed methods without losing exploration performance in different unstructured, cluttered environments.

1. 

   Confidence-rich Localization and Mapping based on Particle Filter for Robotic Exploration Yang Xu, Ronghao Zheng^#, Senlin Zhang, and Meiqin Liu
   2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2022 [Abs] [arXiv] [Video] [Webpage]

   This paper mainly studies the localization and mapping of range sensing robots in the confidence-rich map (CRM) and then extends it to provide a full state estimate for information-theoretic exploration. Most previous works about active simultaneous localization and mapping and exploration always assumed the known robot poses or utilized inaccurate information metrics to approximate pose uncertainty, resulting in imbalanced exploration performance and efficiency in the unknown environment. This inspires us to extend the confidence-rich mutual information (CRMI) with measurable pose uncertainty. Specifically, we propose a Rao- Blackwellized particle filter-based localization and mapping scheme (RBPF -CLAM) for CRM, then we develop a new closed-form weighting method to improve the localization accuracy without scan matching. We further derive the uncertain CRMI (UCRMI) with the weighted particles by a more accurate approximation. Simulations and experimental evaluations show the localization accuracy and exploration performance of the proposed methods.

2. 

   Hierarchical Multi-robot Strategies Synthesis and Optimization under Individual and Collaborative Temporal Logic Specifications Ruofei Bai, Ronghao Zheng^#, Yang Xu, Meiqin Liu, and Senlin Zhang Robotics and Autonomous Systems 2022 [Abs] [arXiv] [Webpage]

   This paper presents a hierarchical framework for multi-robot temporal logic task planning. We assume that each robot has its individual task specification and the robots have to jointly satisfy a global collaborative task specification, both described in finite linear temporal logic. To reduce the overall computational complexity, a central server firstly extracts and decomposes a collaborative task sequence from the automaton corresponding to the collaborative task specification, and allocates the subtasks in the sequence to robots. The robots then synthesize their initial execution strategies based on locally constructed product automatons, which integrate task requirements of the assigned collaborative tasks and their individual task specifications. Further, to reduce robots’ wait time in collaborations, we propose a distributed execution strategy adjusting mechanism to iteratively improve the time efficiency of robots. Finally, we prove the completeness of the proposed framework under assumptions, and analyze its time complexity and optimality. Extensive simulation results verify the scalability and optimization efficiency of the proposed method.

3. 

   Robust Inertial-aided Underwater Localization based on Imaging Sonar Keyframes Yang Xu, Ronghao Zheng^#, Senlin Zhang, and Meiqin Liu IEEE Transactions on Instrumentation and Measurement 2022 [Abs] [arXiv] [Webpage]

   This article focuses on feature-based underwater localization and navigation for autonomous underwater vehicles (AUVs) using 2-D imaging sonar measurements. The sparsity of underwater acoustic features and the loss of elevation angle in sonar images may introduce wrong feature matches or insufficient features for optimization-based underwater localization (i.e., underconstrained/degeneracy cases). This motivates us to propose a novel inertial-aided sliding window optimization framework to improve the estimation accuracy and the robustness to front-end outliers. Concretely, we first discriminate underconstrained/well-constrained sonar frames and define sonar keyframes (SKFs) based on the Jacobian matrix derived from odometry and sonar measurements. To utilize the past well-constrained SKFs mostly, we design a size-adjustable windowed back-end optimization scheme based on singular values. We also prove that the landmark triangulation failure (navigation problem) caused by sonar motion can be solved in 2-D scenes. Comparative simulation and evaluation on a public dataset show that the proposed method outperforms the existing ones in pose estimation and robustness even without loop closure and also ensures the real-time performance for online applications.

1. 

   CRMI: Confidence-rich Mutual Information for Information-theoretic Mapping Yang Xu, Ronghao Zheng^#, Meiqin Liu, and Senlin Zhang IEEE Robotics and Automation Letters (presented at IROS 2021) 2021 [Abs] [Video] [Webpage] [Code]

   This letter focuses on information-theoretic active mapping and exploration with beam-based range sensing robots. Traditional works based on hand-engineered inverse sensor model (ISM) mapping or kernel inference methods lead to imbalanced accuracy and efficiency. This motivates us to propose a new approach to compute mutual information more accurately, based on the continuous belief distribution over the occupancy map and called confidence-rich mutual information (CRMI). Specifically, we explicitly model the measurement dependencies between grid cells within the same measurement cone at each time step and derive the CRMI for each cell on all beams by introducing a more general beam-based sensor cause model (SCM), rather than the customized ISM. The time efficiency for CRMI mapping allows for online implementation as well. Extensive simulations and experiments show the desired exploratory behavior to unexplored and obscured regions for CRMI-based robot controllers in the unstructured and cluttered scene, even in large scale environment.

1. 

   Keyframe-Based Imaging Sonar Localization and Navigation using Elastic Windowed Optimization Yang Xu, Ronghao Zheng^#, Meiqin Liu, and Senlin Zhang
   Global Oceans 2020: Singapore – U.S. Gulf Coast 2020 [Abs] [Webpage]

   This paper reports a sonar keyframe-based elastic windowed optimization scheme for underwater localization and navigation using a 2D forward-looking imaging sonar. Due to the sparse acoustic features and the missing elevation angle in sonar images, degeneracy cases (namely under-constrained), such as relative pose ambiguity, exist in underwater sonar simultaneous localization and mapping (SLAM). To extract and fully utilize informative constraints from past sonar frames, we first identify the degeneracy case and choose sonar keyframes (KFs) using the effective information volume criteria. Secondly, we present an elastic windowed optimization framework in which the window size is adjusted according to certain rules. This scheme is also more robust to outliers from the frontend. Finally, the feasibility and effectiveness of the proposed method are verified through the comparative simulation.