2024-07 Our paper on Tracking with Strided Memory Fusion for Consistent Vector HD Mapping was accepted to ECCV 2024! Congratulations to Jiaqi and other colleagues!
2024-06 Our paper on Mean-Field Control with Envelope Q-Learning for Moving Decentralized Agents in Formation was accepted to IROS 2024! Congratulations to Qiushi!
2024-06 Master Class at SoCS 2024!
2024-02 Our paper on Multi-Robot Connected Fermat Spiral Coverage was accepted to ICAPS 2024! Congratulations to Jingtao!
2023-12 Our paper on Local Search for Large-Scale Multi-Robot Coverage Path Planning was accepted to AAAI 2024! Congratulations to Jingtao!
2023-08 Our paper on Mixed Integer Programming for Multi-Robot Coverage Path Planning was accepted to IEEE Robotics and Automation Letters! Congratulations to Jingtao!
2023-06 Our paper on Soft Actor-Critic with Heuristic-Based Attention for MAPF was accepted to IEEE Robotics and Automation Letters! Congratulations to Qiushi!
2023-04 Our paper on Large-Scale Multi-Robot Rearrangement was accepted to IEEE Robotics and Automation Letters! Congratulations to Baiyu!
2023-02 Paper accepted to ICAPS 2023!
2022-06 Paper accepted to IROS 2022! Congratulations to Qinghong!
2022-06 A Current Robotics Review article on Graph-Based Multi-Robot Path Finding and Planning is available!
2022-01 Paper accepted to ICRA 2022! Congratulations to Xinyi!


I am an Assistant Professor in Computing Science at Simon Fraser University and director of the Autonomous Intelligence and Robotics (AIRob) lab.

My interests are mainly in artificial intelligence, robotics, and machine learning. Specifically, I am interested in topics on automated planning, multi-agent/robot systems, spatio-temporal and constraint reasoning, and applications of probabilistic methods and other topics related to graphs, combinatorial optimization, and algorithms.

Research Opportunities

photo by TourismVancouver

I am always looking for self-motivated students at all levels. See my research highlights here or watch my AAAI-21 New Faculty Highlights talk below to learn more about my research.

If you are interested in working with me on AI, robotics, and multi-agent/robot systems, please mention my name in your application to the SFU CS graduate program. Applicants should also refer to the SFU CS graduate program page for more information on the admission requirements and application deadlines. Eligible students with only a bachelor's degree are encouraged to apply directly to the PhD program.

The SFU main campus is located on the Burnaby Mountain, 12 miles from downtown Vancouver.

Recent Service

Conference and Workshop Organization

Conference Area Chair and (Senior) Program Committee Member

  • AAAI Conference on Artificial Intelligence (AAAI) 2022, 2021, 2020
  • International Joint Conference on Artificial Intelligence (IJCAI) 2022, 2021 (SPC), 2020, 2019
  • International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2021, 2020, 2019
  • International Conference on Automated Planning and Scheduling (ICAPS) 2022, 2021
  • International Conference of the Florida Artificial Intelligence Research Society (FLAIR) 2022, 2021
  • ACM/SIGGRAPH conference on Motion, Interaction and Games (MIG) 2021
  • International Symposium on Multi-Robot and Multi-Agent Systems (MRS) 2021 (AC)
  • International Symposium on Combinatorial Search (SoCS) 2022, 2020

Journal Editing



  • 2014 to 2019, Ph.D. Computer Science, University of Southern California
  • 2012 to 2014, M.Sc. Computer Science, McGill University
  • 2010 to 2012, B.Sc. (First Class with Distinction) Computing Science, Simon Fraser University
  • 2008 to 2010, B.Eng. Computer Science and Technology, Zhejiang University


Recent Publications

  • @inproceedings{LinIROS24,
     author = {Qiushi Lin and Hang Ma},
     booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
     pages = {(in print)},
     title = {MFC-EQ: Mean-Field Control with Envelope Q-Learning for Moving Decentralized Agents in Formation},
     year = {2024}

  • @inproceedings{ChenECCV24,
     author = {Jiacheng Chen and Yuefan Wu and Jiaqi Tan and Hang Ma and Yasutaka Furukawa},
     booktitle = {European Conference on Computer Vision},
     pages = {(in print)},
     title = {MapTracker: Tracking with Strided Memory Fusion for Consistent Vector HD Mapping},
     year = {2024}

  • We introduce the Multi-Robot Connected Fermat Spiral (MCFS), a novel algorithmic framework for Multi-Robot Coverage Path Planning (MCPP) that adapts Connected Fermat Spiral (CFS) from the computer graphics community to multi-robot coordination for the first time. MCFS uniquely enables the orchestration of multiple robots to generate coverage paths that contour around arbitrarily shaped obstacles, a feature that is notably lacking in traditional methods. Our framework not only enhances area coverage and optimizes task performance, particularly in terms of makespan, for workspaces rich in irregular obstacles but also addresses the challenges of path continuity and curvature critical for non-holonomic robots by generating smooth paths without decomposing the workspace. MCFS solves MCPP by constructing a graph of isolines and transforming MCPP into a combinatorial optimization problem, aiming to minimize the makespan while covering all vertices. Our contributions include developing a unified CFS version for scalable and adaptable MCPP, extending it to MCPP with novel optimization techniques for cost reduction and path continuity and smoothness, and demonstrating through extensive experiments that MCFS outperforms existing MCPP methods in makespan, path curvature, coverage ratio, and overlapping ratio. Our research marks a significant step in MCPP, showcasing the fusion of computer graphics and automated planning principles to advance the capabilities of multi-robot systems in complex environments. Our code is publicly available at
     author = {Jingtao Tang and Hang Ma},
     booktitle = {International Conference on Automated Planning and Scheduling},
     pages = {579--587},
     title = {Multi-Robot Connected Fermat Spiral Coverage},
     year = {2024}

  • We study graph-based Multi-Robot Coverage Path Planning (MCPP) that aims to compute coverage paths for multiple robots to cover all vertices of a given 2D grid terrain graph $G$. Existing graph-based MCPP algorithms first compute a tree cover on $G$---a forest of multiple trees that cover all vertices---and then employ the Spanning Tree Coverage (STC) paradigm to generate coverage paths on the decomposed graph $D$ of the terrain graph $G$ by circumnavigating the edges of the computed trees, aiming to optimize the makespan (i.e., the maximum coverage path cost among all robots). In this paper, we take a different approach by exploring how to systematically search for good coverage paths directly on $D$. We introduce a new algorithmic framework, called LS-MCPP, which leverages a local search to operate directly on $D$. We propose a novel standalone paradigm, Extended-STC (ESTC), that extends STC to achieve complete coverage for MCPP on any decomposed graphs, even those resulting from incomplete terrain graphs. Furthermore, we demonstrate how to integrate ESTC with three novel types of neighborhood operators into our framework to effectively guide its search process. Our extensive experiments demonstrate the effectiveness of LS-MCPP, consistently improving the initial solution returned by two state-of-the-art baseline algorithms that compute suboptimal tree covers on $G$, with a notable reduction in makespan by up to 35.7% and 30.3%, respectively. Moreover, LS-MCPP consistently matches or surpasses the results of optimal tree cover computation, achieving these outcomes with orders of magnitude faster runtime, thereby showcasing its significant benefits for large-scale real-world coverage tasks.
     author = {Jingtao Tang and Hang Ma},
     booktitle = {{AAAI} Conference on Artificial Intelligence},
     pages = {17567--17574},
     title = {Large-Scale Multi-Robot Coverage Path Planning via Local Search},
     year = {2024}

  • We investigate time-optimal Multi-Robot Coverage Path Planning (MCPP) for both unweighted and weighted terrains, which aims to minimize the coverage time, defined as the maximum travel time of all robots. Specifically, we focus on a reduction from MCPP to Min-Max Rooted Tree Cover (MMRTC). For the first time, we propose a Mixed Integer Programming (MIP) model to optimally solve MMRTC, resulting in an MCPP solution with a coverage time that is provably at most four times the optimal. Moreover, we propose two suboptimal yet effective heuristics that reduce the number of variables in the MIP model, thus improving its efficiency for large-scale MCPP instances. We show that both heuristics result in reduced-size MIP models that remain complete (i.e., guaranteed to find a solution if one exists) for all MMRTC instances. Additionally, we explore the use of model optimization warm-startup to further improve the efficiency of both the original MIP model and the reduced-size MIP models. We validate the effectiveness of our MIP-based MCPP planner through experiments that compare it with two state-of-the-art MCPP planners on various instances, demonstrating a reduction in the coverage time by an average of $27.65%$ and $23.24%$ over them, respectively.
     author = {Jingtao Tang and Hang Ma},
     journal = {IEEE Robotics and Automation Letters},
     number = {10},
     pages = {6491--6498},
     title = {Mixed Integer Programming for Time-Optimal Multi-Robot Coverage Path Planning with Efficient Heuristics},
     volume = {8},
     year = {2023}

  • Multi-Agent Path Finding (MAPF) is a crucial component for many large-scale robotic systems, where agents must plan their collision-free paths to their given goal positions. Recently, multi-agent reinforcement learning has been introduced to solve the partially observable variant of MAPF by learning a decentralized single-agent policy in a centralized fashion based on each agent's partial observation. However, existing learning-based methods are ineffective in achieving complex multi-agent cooperation, especially in congested environments, due to the non-stationarity of this setting. To tackle this challenge, we propose a multi-agent actor-critic method called Soft Actor-Critic with Heuristic-Based Attention (SACHA), which employs novel heuristic-based attention mechanisms for both the actors and critics to encourage cooperation among agents. SACHA learns a neural network for each agent to selectively pay attention to the shortest path heuristic guidance from multiple agents within its field of view, thereby allowing for more scalable learning of cooperation. SACHA also extends the existing multi-agent actor-critic framework by introducing a novel critic centered on each agent to approximate $Q$-values. Compared to existing methods that use a fully observable critic, our agent-centered multi-agent actor-critic method results in more impartial credit assignment and better generalizability of the learned policy to MAPF instances with varying numbers of agents and types of environments. We also implement SACHA(C), which embeds a communication module in the agent's policy network to enable information exchange among agents. We evaluate both SACHA and SACHA(C) on a variety of MAPF instances and demonstrate decent improvements over several state-of-the-art learning-based MAPF methods with respect to success rate and solution quality.
     author = {Qiushi Lin and Hang Ma},
     journal = {IEEE Robotics and Automation Letters},
     number = {8},
     pages = {2377--3766},
     title = {SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially Observable Multi-Agent Path Finding},
     volume = {8},
     year = {2023}

  • Full list of publications