News
About
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
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
- Co-Chair, International Symposium on Combinatorial Search (SoCS) 2021
- Systems Demo Co-Chair, International Conference on Automated Planning and Scheduling (ICAPS) 2021
- Co-Chair, 3rd International Workshop on Multi-Agent Path Finding at IJCAI 2019
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
- Guest Editor, SN Applied Sciences Topical Collection on Distributed Mobile Robotic Systems
Teaching
- CMPT 417/827: Intelligent Systems (Fall 2021, Spring 2021, Spring 2020)
Education
- 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
Miscellaneous
- My Erdös number is 2 : Paul Erdös 0 ↔ Craig Tovey 1 ↔ Hang Ma 2.
Recent Publications
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We study Multi-Robot Coverage Path Planning (MCPP) on a 4-neighbor 2D grid $G$, which aims to compute paths for multiple robots to cover all cells of $G$. Traditional approaches are limited as they first compute coverage trees on a quadrant coarsened grid $\mathcalH$ and then employ the Spanning Tree Coverage (STC) paradigm to generate paths on $G$, making them inapplicable to grids with partially obstructed $2 \times 2$ blocks. To address this limitation, we reformulate the problem directly on $G$, revolutionizing grid-based MCPP solving and establishing new NP-hardness results. We introduce Extended-STC (ESTC), a novel paradigm that extends STC to ensure complete coverage with bounded suboptimality, even when $\mathcalH$ includes partially obstructed blocks. Furthermore, we present LS-MCPP, a new algorithmic framework that integrates ESTC with three novel types of neighborhood operators within a local search strategy to optimize coverage paths directly on $G$. Unlike prior grid-based MCPP work, our approach also incorporates a versatile post-processing procedure that applies Multi-Agent Path Finding (MAPF) techniques to MCPP for the first time, enabling a fusion of these two important fields in multi-robot coordination. This procedure effectively resolves inter-robot conflicts and accommodates turning costs by solving a MAPF variant, making our MCPP solutions more practical for real-world applications. Extensive experiments demonstrate that our approach significantly improves solution quality and efficiency, managing up to 100 robots on grids as large as $256 \times 256$ within minutes of runtime. Validation with physical robots confirms the feasibility of our solutions under real-world conditions. A project page with code, demo videos, and additional resources is available at: https://sites.google.com/view/lsmcpp.
@article{TangTRO25, author = {Jingtao Tang and Zining Mao and Hang Ma}, journal = {IEEE Transactions on Robotics}, number = {}, pages = {(in print)}, title = {Large-Scale Multi-Robot Coverage Path Planning on Grids with Path Deconfliction}, volume = {}, year = {2025} } -
We study a decentralized version of Moving Agents in Formation (MAiF), a variant of Multi-Agent Path Finding aiming to plan collision-free paths for multiple agents with the dual objectives of reaching their goals quickly while maintaining a desired formation. The agents must balance these objectives under conditions of partial observation and limited communication. The formation maintenance depends on the joint state of all agents, whose dimensionality increases exponentially with the number of agents, rendering the learning process intractable. Additionally, learning a single policy that can accommodate different linear preferences for these two objectives presents a significant challenge. In this paper, we propose Mean-Field Control with Envelop Q-learning (MFC-EQ), a scalable and adaptable learning framework for this bi-objective multi-agent problem. We approximate the dynamics of all agents using mean-field theory while learning a universal preference-agnostic policy through envelop Q-learning. Our empirical evaluation of MFC-EQ across numerous instances shows that it outperforms state-of-the-art centralized MAiF baselines. Furthermore, MFC-EQ effectively handles more complex scenarios where the desired formation changes dynamically -- a challenge that existing MAiF planners cannot address.
@inproceedings{LinIROS24, author = {Qiushi Lin and Hang Ma}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems}, pages = {14156--14163}, title = {MFC-EQ: Mean-Field Control with Envelope Q-Learning for Moving Decentralized Agents in Formation}, year = {2024} } -
This paper presents a vector HD-mapping algorithm that formulates the mapping as a tracking task and uses a history of memory latents to ensure consistent reconstructions over time. Our method, MapTracker, accumulates a sensor stream into memory buffers of two latent representations: 1) Raster latents in the bird’s-eye-view (BEV) space and 2) Vector latents over the road elements (i.e., pedestrian-crossings, lane-dividers, and road-boundaries). The approach borrows the query propagation paradigm from the tracking literature that explicitly associates tracked road elements from the previous frame to the current, while fusing a subset of memory latents selected with distance strides to further enhance temporal consistency. A vector latent is decoded to reconstruct the geometry of a road element. The paper further makes benchmark contributions by 1) Improving processing code for existing datasets to produce consistent ground truth with temporal alignments and 2) Augmenting existing mAP metrics with consistency checks. MapTracker significantly outperforms existing methods on both nuScenes and Agroverse2 datasets by over 8% and 19% on the conventional and the new consistency-aware metrics, respectively. The code will be available on our project page: https://map-tracker.github.io.
@inproceedings{ChenECCV24, author = {Jiacheng Chen and Yuefan Wu and Jiaqi Tan and Hang Ma and Yasutaka Furukawa}, booktitle = {European Conference on Computer Vision}, pages = {90--107}, 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 https://github.com/reso1/MCFS.
@inproceedings{TangICAPS24, 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.
@inproceedings{TangAAAI24, 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.
@article{TangRAL23, 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} }
Full list of publications