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Recent Publications

• CREST: Constraint-Release Execution for Multi-Robot Warehouse Shelf Rearrangement.
  Jiaqi Tan, Yudong Luo, Sophia Huang, Yifan Yang and Hang Ma.
  IEEE Robotics and Automation Letters (RA-L) 11(5), March 2026.
  [ pdf] [ abstract] [ bibtex] [ video] [ publisher] [ github]

  Double-Deck Multi-Agent Pickup and Delivery (DD-MAPD) models the multi-robot shelf rearrangement problem in automated warehouses. MAPF-DECOMP is a recent framework that first computes collision-free shelf trajectories with a MAPF solver and then assigns agents to execute them. While efficient, it enforces strict trajectory dependencies, often leading to poor execution quality due to idle agents and unnecessary shelf switching. We introduce CREST, a new execution framework that achieves more continuous shelf carrying by proactively releasing trajectory constraints during execution. Experiments on diverse warehouse layouts show that CREST consistently outperforms MAPF-DECOMP, reducing metrics related to agent travel, makespan, and shelf switching by up to 40.5%, 33.3%, and 44.4%, respectively, with even greater benefits under lift/place overhead. These results underscore the importance of execution-aware constraint release for scalable warehouse rearrangement. Code and data are available at https://github.com/ChristinaTan0704/CREST.

  @article{TanRAL26,
    author = {Jiaqi Tan and Yudong Luo and Sophia Huang and Yifan Yang and Hang Ma},
    title = {CREST: Constraint-Release Execution for Multi-Robot Warehouse Shelf Rearrangement},
    journal = {IEEE Robotics and Automation Letters},
    volume = {11},
    number = {5},
    pages = {6447--6454},
    year = {2026}
  }

• GHOST: Solving the Traveling Salesman Problem on Graphs of Convex Sets.
  Jingtao Tang and Hang Ma.
  AAAI Conference on Artificial Intelligence (AAAI), Singapore, January 2026.
  [ pdf] [ abstract] [ bibtex] [ project] [ github]

  We study GCS-TSP, a variant of the Traveling Salesman Problem (TSP) defined over a Graph of Convex Sets (GCS)—a powerful representation for trajectory planning that decomposes the configuration space into convex regions connected by a sparse graph. In GCS-TSP, edge costs are not fixed but depend on the specific trajectory passing through each convex region, making classical TSP methods inapplicable. We introduce GHOST, a hierarchical framework that optimally solves GCS-TSP by combining combinatorial tour search with convex trajectory optimization. GHOST systematically explores tours on a complete graph induced by the GCS, using a novel abstract-path-unfolding algorithm to compute admissible lower bounds that guide best-first search at both the high level (over tours) and the low level (over feasible GCS paths realizing the tour). These bounds provide strong pruning power, reducing unnecessary optimization calls. We prove that GHOST guarantees optimality and present a bounded-suboptimal variant for time-critical settings. Experiments show that GHOST is orders-of-magnitude faster than unified mixed-integer convex programming baseline while uniquely handling complex problems involving high-order continuity constraints and incomplete GCSs.

  @inproceedings{TangAAAI26,
    author = {Jingtao Tang and Hang Ma},
    title = {GHOST: Solving the Traveling Salesman Problem on Graphs of Convex Sets},
    booktitle = {{AAAI} Conference on Artificial Intelligence},
    pages = {36421--36428},
    year = {2026}
  }

• Search-Based Spatiotemporal and Multi-Robot Motion Planning on Graphs of Space-Time Convex Sets.
  Tang, Jingtao, Mao, Zining, Yang, Lufan and Ma, Hang.
  arXiv preprint arXiv:2607.00444, 2026.
  [ pdf] [ bibtex] [ project] [ github]

  @article{TangARXIV26,
    author = {Tang, Jingtao and Mao, Zining and Yang, Lufan and Ma, Hang},
    title = {Search-Based Spatiotemporal and Multi-Robot Motion Planning on Graphs of Space-Time Convex Sets},
    journal = {arXiv preprint arXiv:2607.00444},
    year = {2026}
  }

• Combinatorial Route Optimization Using Near-Training-Free Foundation Models.
  Nguyen Gia Hien Vu, Yifan Tang, Rey Lim, Yifan Yang, Hang Ma, Ke Wang and G Gary Wang.
  Eng 7(8), 2026.
  [ pdf] [ abstract] [ bibtex] [ publisher]

  Combinatorial Route Optimization (CRO) problems, such as the Vehicle Routing Problem (VRP) or the Travelling Salesman Problem (TSP), are commonly seen in scheduling, logistics, and transportation. While current machine learning (ML) methods can overcome certain limitations of traditional approaches, including exact and heuristic algorithms, they typically require substantial computational resources, large training datasets, and carefully designed models, thereby limiting their scalability and practical deployment. In this paper, we develop a method to address such concerns in a data-efficient and near-training-free manner using foundation models. We select TSP, one of the most well-known combinatorial optimization problems, to solve in our experiments and employ the Tabular Prior-Data Fitted Network (TabPFN), one of the newly designed foundation models. Specifically, we develop a node-based formulation that converts TSP into a sequence of localized prediction tasks and constructs a complete route through in-context learning provided by TabPFN. The proposed method enables TabPFN, a model developed for regression and classification, to be applied to CRO problems with only one TSP sample for fine-tuning. We evaluate the proposed method across varying TSP instance sizes and demonstrate that our approach generalizes effectively without retraining, maintains competitive solution quality, and exhibits promising scalability. These findings suggest that CRO problems can be approached through foundation models, enabling scalability as well as generating rapidly deployable solutions with near-training-free adaptation.

  @article{VuENG26,
    author = {Nguyen Gia Hien Vu and Yifan Tang and Rey Lim and Yifan Yang and Hang Ma and Ke Wang and G Gary Wang},
    title = {Combinatorial Route Optimization Using Near-Training-Free Foundation Models},
    journal = {Eng},
    volume = {7},
    number = {8},
    pages = {375},
    year = {2026}
  }

• Space-Time Graphs of Convex Sets for Multi-Robot Motion Planning.
  Jingtao Tang, Zining Mao, Lufan Yang and Hang Ma.
  IEEE/RSJ International Conference on Intelligent Robots and System (IROS), Hangzhou, China, October 2025.
  [ pdf] [ abstract] [ bibtex] [ video] [ publisher] [ project] [ github]
  Best Paper Award at the RSS 2025 Workshop on Scalable and Resilient Multi-Robot Systems

  We address the Multi-Robot Motion Planning (MRMP) problem of computing collision-free trajectories for multiple robots in shared continuous environments. While existing frameworks effectively decompose MRMP into singlerobot subproblems, spatiotemporal motion planning with dynamic obstacles remains challenging, particularly in cluttered or narrow-corridor settings. We propose Space-Time Graphs of Convex Sets (ST-GCS), a novel planner that systematically covers the collision-free space-time domain with convex sets instead of relying on random sampling. By extending Graphs of Convex Sets (GCS) into the time dimension, ST-GCS formulates time-optimal trajectories in a unified convex optimization that naturally accommodates velocity bounds and flexible arrival times. We also propose Exact Convex Decomposition (ECD) to "reserve" trajectories as spatiotemporal obstacles, maintaining a collision-free space-time graph of convex sets for subsequent planning. Integrated into two prioritized-planning frameworks, ST-GCS consistently achieves higher success rates and better solution quality than state-of-the-art sampling-based planners—often at orders-of-magnitude faster runtimes—underscoring its benefits for MRMP in challenging settings. Project page: https://sites.google.com/view/stgcs.

  @inproceedings{TangIROS25,
    author = {Jingtao Tang and Zining Mao and Lufan Yang and Hang Ma},
    title = {Space-Time Graphs of Convex Sets for Multi-Robot Motion Planning},
    booktitle = {{IEEE/RSJ} International Conference on Intelligent Robots and System},
    pages = {8683--8690},
    year = {2025}
  }

• Reevaluation of Large Neighborhood Search for MAPF: Findings and Opportunities.
  Jiaqi Tan*, Yudong Luo*, Jiaoyang Li and Hang Ma. (* Equal contribution)
  International Symposium on Combinatorial Search (SoCS), Glasgow, United Kingdom, August 2025.
  [ pdf] [ abstract] [ bibtex] [ github] (Position Paper)

  Multi-Agent Path Finding (MAPF) aims to arrange collision-free goal-reaching paths for a group of agents. Anytime MAPF solvers based on large neighborhood search (LNS) have gained prominence recently due to their flexibility and scalability, leading to a surge of methods, especially those leveraging machine learning, to enhance neighborhood selection. However, several pitfalls exist and hinder a comprehensive evaluation of these new methods, which mainly include: 1) Lower than actual or incorrect baseline performance; 2) Lack of a unified evaluation setting and criterion; 3) Lack of a codebase or executable model for supervised learning methods. To address these challenges, we introduce a unified evaluation framework, implement prior methods, and conduct an extensive comparison of prominent methods. Our evaluation reveals that rule-based heuristics serve as strong baselines, while current learning-based methods show no clear advantage on time efficiency or improvement capacity. Our extensive analysis also opens up new research opportunities for improving MAPF-LNS, such as targeting high-delayed agents, applying contextual algorithms, optimizing replan order and neighborhood size, where machine learning can potentially be integrated.

  @inproceedings{TanSOCS25,
    author = {Jiaqi Tan and Yudong Luo and Jiaoyang Li and Hang Ma},
    title = {Reevaluation of Large Neighborhood Search for MAPF: Findings and Opportunities},
    booktitle = {International Symposium on Combinatorial Search},
    pages = {212--220},
    year = {2025}
  }

Full list of publications