Singapore

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.

AAAI 2026

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

model predictive path integral

multi-robot collision avoidance

multi-robot navigation

multi-agent motion planning

robot

reinforcement learning

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).

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AAMAS 2022

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

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 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.

Flow-Based Guidance Framework with Flex Distribution for $w$-Optimal Multi-Agent Path Finding

State-of-the-art multi-robot kinodynamic motion planners struggle to handle more than a few robots due to high computational burden, which limits their scalability and results in slow planning time.
In this work, we combine the scalability and speed of modern multi-agent path finding (MAPF) algorithms with the dynamic-awareness of kinodynamic planners to address these limitations.
To this end, we propose discontinuity-Bounded LaCAM (db-LaCAM), a planner that utilizes a precomputed set of motion primitives that respect robot dynamics to generate horizon-length motion sequences, while allowing a user-defined discontinuity between successive motions.
The planner db-LaCAM is resolution-complete with respect to motion primitives and supports arbitrary robot dynamics.
Extensive experiments demonstrate that db-LaCAM scales efficiently to scenarios with up to $50$ robots, achieving up to ten times faster runtime compared to state-of-the-art planners, while maintaining comparable solution quality.
The approach is validated in both 2D and 3D environments with dynamics such as the unicycle and 3D double integrator.
We demonstrate the safe execution of trajectories planned with db-LaCAM in two distinct physical experiments involving teams of flying robots and car-with-trailer robots.

db-LaCAM: Fast and Scalable Multi-Robot Kinodynamic Motion Planning with Discontinuity-Bounded Search and Lightweight MAPF

Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics and logistics, where multiple agents must reach their goals without collisions. While deep Multi-Agent Reinforcement Learning methods have recently shown impressive scalability and adaptability, their black-box nature hinders interpretability and trust—crucial aspects for deployment in real-world systems. In this work, we propose an interpretable policy distillation framework for MAPF. We first formulate MAPF as a stochastic game and execute a trained neural policy across diverse environments to build a large dataset of state–action pairs. We then distill this neural policy into a decision tree model that captures its underlying decision rules while maintaining strong performance. Through extensive evaluation, we analyze the trade-off between interpretability and performance, demonstrating that our distilled models achieve high fidelity to the original policy while providing transparent, human-understandable reasoning about agent behavior.

Interpretable Multi-Agent Path Finding via Decision Tree Extraction from Neural Policies

Multi-Agent Path Finding (MAPF) is a representative multi-agent coordination problem, where multiple agents are required to navigate to their respective goals without collisions. Solving MAPF optimally is known to be NP-hard, leading to the adoption of learning-based approaches to alleviate the online computational burden. Prevailing approaches, such as Graph Neural Networks (GNNs), are typically constrained to *pairwise* message passing between agents. However, this limitation leads to suboptimal behaviours and critical issues, such as attention dilution, particularly in dense environments where group (i.e. beyond just two agents) coordination is most critical. Despite the importance of such higher-order interactions, existing approaches have not been able to fully explore them. To address this representational bottleneck, we introduce HMAGAT (Hypergraph Multi-Agent Attention Network), a novel architecture that leverages attentional mechanisms over directed hypergraphs to explicitly capture group dynamics. Empirically, HMAGAT establishes a new state-of-the-art among learning-based MAPF solvers: e.g., despite having just 1M parameters and being trained on 100$\times$ less data, it outperforms the current SoTA 85M parameter model. Through detailed analysis of HMAGAT's attention values, we demonstrate how hypergraph representations mitigate the attention dilution inherent in GNNs and capture complex interactions where pairwise methods fail. Our results illustrate that appropriate inductive biases are often more critical than the training data size or sheer parameter count for multi-agent problems.

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CAMAR: Continuous Actions Multi-Agent Routing

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Context-Aware Modelling for Multi-Robot Systems Under Uncertainty

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CAMAR: Continuous Actions Multi-Agent Routing

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Context-Aware Modelling for Multi-Robot Systems Under Uncertainty

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