Singapore

Multi-agent pathfinding (MAPF) is the problem of finding collision-free paths for a set of agents in a shared environment, typically represented as a graph. One of the approaches to solving MAPF represents the problem as a Boolean satisfiability problem. However, due to the encoding required to represent the valid paths, this method can produce extremely large Boolean formulas, both in terms of variables and clauses. Herein, we propose two encodings of MAPF designed for SAT Modulo Theories solvers. Our approach delegates all the valid path reasoning to a monotonic theory supporting source-target connectivity. This is then combined with a 2-SAT Boolean formula to prevent collisions between agents. Together, these components create an effective separation of concerns: the SAT solver focuses on resolving conflicts, while the theory solver handles the connectivity constraints. Our experiments are conducted in both makespan and sum of costs optimisation settings, empirically demonstrating a notable reduction in both the size of the MAPF encoding and the time required to generate it. In addition, when fixing the SAT solver across experiments, results demonstrate considerable performance improvements when transitioning from pure SAT to our proposed SMT encodings.

AAAI 2026

Modelling Multi-Agent Pathfinding Problems by Integrating Connectivity and No-Collision Constraints

smt modelling

sat modelling

multi-agent pathfinding

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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AAAI 2025

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



workshop paper

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Multi-agent pathfinding (MAPF) is a widely used abstraction for multi-robot trajectory planning problems, where multiple homogeneous agents move simultaneously within a shared environment. Although solving MAPF optimally is NP-hard, scalable and efficient solvers are critical for real-world applications such as logistics and search-and-rescue. To this end, the research community has proposed various decentralized suboptimal MAPF solvers that leverage machine learning. Such methods frame MAPF (from a single agent perspective) as Dec-POMDP when at each time step an agent has to decide an action based on the local observation and typically solve the problem via reinforcement learning or imitation learning. We follow the same approach but additionally introduce a learnable communication module tailored to increase the level of cooperation between the agents via efficient feature sharing. We present the Local Communication for Multi-agent Pathfinding (LC-MAPF), a foundation model that applies multi-round communication between neighboring agents to exchange information and improve their coordination. Our experiments show that the introduced method outperforms the existing learning-based MAPF solvers, including IL and RL based approaches, across diverse metrics in a diverse range of (unseen) test scenarios. Remarkably, the introduced communication mechanism does not compromise the scalability LC-MAPF, which is a common bottleneck for communication-based MAPF solvers.

Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding

One important component of large industrial electrical projects is the placement of cables that are routed between various pieces of equipment. Many large contractors are still determining routes manually through informal walkthroughs and visual assessment of possible paths. Thus, there is significant room for automation and improvement in this process. This paper describes the general problem, relating it to established problems such as the Multi-Agent Pathfinding Problem (MAPF). We cover some of the practical complications of the real-world problem, describe several abstractions of the problem, and then discuss possible algorithms and approaches for solutions that could be deployed in the real world.

Optimization of Cable Routing During Construction

Decentralized collision avoidance remains a core challenge for scalable multi-robot systems. One of the promising approaches to tackle this problem is Model Predictive Path Integral (MPPI) -- a framework that is naturally suited to handle any robot motion model and provides strong theoretical guarantees. Still, in practice MPPI-based controller may provide suboptimal trajectories as its performance relies heavily on uninformed random sampling. In this work, we introduce CoRL-MPPI, a novel fusion of Cooperative Reinforcement Learning and MPPI to address this limitation. We train an action policy (approximated as deep neural network) in simulation that learns local cooperative collision avoidance behaviors. This learned policy is then embedded into the MPPI framework to guide its sampling distribution, biasing it towards more intelligent and cooperative actions. Notably, CoRL-MPPI preserves all the theoretical guarantees of regular MPPI. We evaluate our approach in dense, dynamic simulation environments against state-of-the-art baselines, including ORCA, BVC, and a multi-agent MPPI implementation. Our results demonstrate that CoRL-MPPI significantly improves navigation efficiency (measured by success rate and makespan) and safety, enabling agile and robust multi-robot navigation.

CoRL-MPPI: Enhancing MPPI With Learnable Behaviours For Efficent And Provably-Safe Multi-Robot Collision Avoidance

Multi-agent reinforcement learning (MARL) is a powerful paradigm for solving cooperative and competitive decision-making problems. While many MARL benchmarks have been proposed, few combine continuous state and action spaces with challenging coordination and planning tasks. We introduce CAMAR, a new MARL benchmark designed explicitly for multi-agent pathfinding in environments with continuous actions. CAMAR supports cooperative and competitive interactions between agents and runs efficiently at up to 100,000 environment steps per second. We also propose a three-tier evaluation protocol to better track algorithmic progress and enable deeper analysis of performance. In addition, CAMAR allows the integration of classical planning methods such as RRT and RRT* into MARL pipelines. We use them as standalone baselines and combine RRT* with popular MARL algorithms to create hybrid approaches. We provide a suite of test scenarios and benchmarking tools to ensure reproducibility and fair comparison. Experiments show that CAMAR presents a challenging and realistic testbed for the MARL community.

CAMAR: Continuous Actions Multi-Agent Routing

Multi-Agent Motion Planning is a problem of finding a set of collision-free trajectories, one for each agent, to move from their start configurations to their goal configurations while minimizing the sum of travel time. To solve MAMP, we propose Dual Conflict-Based Search (Dual-CBS), which combines the search and sampling approaches in a hierarchical manner. Dual-CBS first decouples the configuration space into grids and finds a set of grid-based paths. Then, it samples one trajectory for each agent, which is a sequence of configurations within its grid-based path. In comparison to approaches that find collision-free trajectories on a shared roadmap, Dual-CBS does not introduce heavy computational overhead in constructing such a roadmap. Meanwhile, it maintains the mobility of agents for efficiently resolving collisions. In comparison to the state-of-the-art MAMP approach, Simultaneous Sampling-and-Search (SSSP), which heavily relies on local collision avoidance, Dual-CBS guides the sampled trajectories with the grid-based paths and thus finds solutions with a lower sum of travel time.

Dual-CBS: A Hierarchical Approach via Conflict-Based Search and Sampling for Multi-Agent Motion Planning

Recently, multi-robot systems have gained significant attention for their promise of scalable efficiency, reliability, and cost savings. A crucial capability is collaborative transportation, where a team of robots works together to transport a payload. However, key challenges remain, such as potential conflicts between team-level decisions and individual-level robot controls, team kinematic constraints imposed by the robot-payload coupling, and diverse obstacles encountered in 3D terrain. We present Collaborative Quadruped Transportation with Constrained Diffusion (CQTD), enabling a team of closely coupled quadruped robots to collaboratively transport a payload across 3D terrain. A diffusion-based upper level learns terrain-aware team-level trajectories satisfying team kinematic constraints due to the payload coupling, while a lower level optimizes velocity controls of individual robots satisfying collision and anisotropic velocity constraints. Experiments in high-fidelity simulations and on real-world quadruped robot teams demonstrate that CQTD outperforms baseline methods in challenging 3D terrain scenarios requiring closely-coupled collaboration between the quadruped robots.

Collaborative Quadruped Transportation in 3D Terrain with Constrained Diffusion

Recent advances in multi-agent reinforcement learning (MARL) have demonstrated success in numerous challenging domains and environments, but typically require specialized models for each task. In this work, we propose a coherent methodology that makes it possible for a single GPT-based model to learn and perform well across diverse MARL environments and tasks, including collision-avoidance and coordination problems (such as multi-agent path finding scenarios demonstrated in POGEMA), alongside established benchmarks like StarCraft Multi-Agent Challenge and Google Research Football. Our method, MARL-GPT, applies offline reinforcement learning to train at scale on expert trajectories (400M for SMACv2, 100M for GRF, and 1B for POGEMA) combined with a single transformer-based observation encoder that requires no task-specific tuning. By leveraging offline RL, we address the long-horizon planning and coordination challenges inherent in MAPF-like problems, enabling efficient learning without costly online environment interaction. Experiments show that MARL-GPT achieves competitive performance compared to specialized baselines in all tested environments. Thus, our findings suggest that it is, indeed, possible to build a multi-task transformer-based model for a wide variety of (significantly different) multi-agent problems paving the way to the fundamental MARL model (akin to ChatGPT, Llama, Mistral etc. in natural language modeling).

MARL-GPT: Foundation Model for Multi-Agent Reinforcement Learning

Large language model (LLM) agents have shown increasing promise for collaborative task completion. However, existing multi-agent frameworks often rely on static workflows, fixed roles, and limited inter-agent communication, reducing their effectiveness in open-ended, high-complexity domains. This paper proposes a coordination framework that enables adaptiveness through three core mechanisms: dynamic task routing, bidirectional feedback, and parallel agent evaluation. The framework allows agents to reallocate tasks based on confidence and workload, exchange structured critiques to iteratively improve outputs, and—crucially—compete on high-ambiguity subtasks with evaluator-driven selection of the most suitable result. We instantiate these principles in a modular architecture and demonstrate substantial improvements in factual coverage, coherence, and efficiency over static and partially adaptive baselines. Our findings highlight the benefits of incorporating both adaptiveness and structured competition in multi-agent LLM systems.

Parallelism Meets Adaptiveness: Scalable Documents Understanding in Multi-Agent LLM Systems

Priority Inheritance and Backtracking (PIBT) has demonstrated significant success as a heuristic for large-scale Multi-Agent Path Finding (MAPF), but its application has been limited to scenarios where all robots move at uniform speeds. This work addresses the critical question of extending PIBT's capabilities to heterogeneous fleets, where robots may vary in size and speed. We introduce a novel collision-checking-based reservation system integrated within the WinPIBT framework and a new backtracking mechanism. Heterogeneous PIBT (HetPIBT) effectively scales to hundreds of robots of various sizes. Our findings confirm that this scheme enables the application of heterogeneous MAPF to large robot populations. To facilitate further research, we also provide a new set of heterogeneous MAPF benchmark scenarios along with a Python package for the generation of such problems and visualization tools. However, we observe that HetPIBT, similar to its homogeneous counterpart, can yield suboptimal solutions. We provide an open source Rust package which users can build upon.

Extending PiBT to support Heterogenous Robot Fleets

Multi-Agent Path Finding (MAPF) is a one-shot problem of finding collision-free paths in a shared environment while minimizing the sum of the agents' travel times.
Since solving MAPF optimally is NP-hard, $w$-optimal algorithms such as Explicit Estimation Conflict-Based Search (EECBS) have been used to speed up the search while providing a guarantee on the solution quality.
However, the scalability of EECBS is limited in large-scale MAPF instances.
While EECBS can be accelerated for regularly structured environments, such as Kiva warehouses, by utilizing specialized guidance heuristics, these heuristics are ineffective in more general and large-scale environments.
To fill this gap, we propose the \textit{Flow-Based Guidance Framework}, a general two-phase process that simulates a list of paths and then generates the \textit{Flow-Based Guidance Heuristic} (FH) without making prior assumptions about the environment's structure.
We identify features that distinguish $w$-optimal MAPF from other MAPF variants and propose strategies to enhance its effectiveness for guidance, complemented by the flex distribution technique from EECBS.
The empirical evaluation demonstrates that our FH significantly reduces collisions, thereby achieving higher success rates than the state-of-the-art within 60 seconds.

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