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Recent Publications

• 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), August 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}
  }

• 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}
  }

• Goal Staying Makes Sum-of-Costs Anonymous Multi-Agent Path Finding NP-Hard.
  Hang Ma.
  arXiv preprint arXiv:2608.28658, 2026.
  [ pdf] [ bibtex]

  @article{MaARXIV26,
    author = {Hang Ma},
    title = {Goal Staying Makes Sum-of-Costs Anonymous Multi-Agent Path Finding NP-Hard},
    journal = {arXiv preprint arXiv:2608.28658},
    year = {2026}
  }

• Unified Branch-and-Bound Search for the Steiner Traveling Salesman Problem on Graphs of Convex Sets.
  Jingtao Tang and Hang Ma.
  arXiv preprint arXiv:2608.21319, 2026.
  [ pdf] [ bibtex] [ project]

  @article{TangARXIV26b,
    author = {Jingtao Tang and Hang Ma},
    title = {Unified Branch-and-Bound Search for the Steiner Traveling Salesman Problem on Graphs of Convex Sets},
    journal = {arXiv preprint arXiv:2608.21319},
    year = {2026}
  }

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

  @article{TangARXIV26a,
    author = {Jingtao Tang and Zining Mao and Lufan Yang and Hang Ma},
    title = {Search-Based Spatiotemporal and Multi-Robot Motion Planning on Graphs of Space-Time Convex Sets},
    journal = {arXiv preprint arXiv:2607.00444},
    year = {2026}
  }

Full list of publications