Sentinel: Embodied Cooperative Spatial Reasoning and Planning

• URL: http://arxiv.org/abs/2605.26239v1
• Date: Mon, 25 May 2026 18:04:41 GMT
• Title: Sentinel: Embodied Cooperative Spatial Reasoning and Planning
• Abstract summary: We study Cooperative Spatial Intelligence, the ability of decentralized embodied agents to coordinate under dynamic environmental constraints.<n>We introduce Sentinel Challenge, a benchmark where multiple agents must communicate in natural language to agree on a mutually safe and convenient meeting point.<n>We propose CoSaR, a framework that bridges the high-level communication and planning abilities of foundation models with the precision of classical spatial navigation algorithms.
• Score: 29.50555565754175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we study Cooperative Spatial Intelligence, the ability of decentralized embodied agents to coordinate effectively under dynamic environmental constraints across city-scale outdoor domains. We introduce Sentinel Challenge, a benchmark where multiple decentralized embodied agents must communicate in natural language to agree on a mutually safe and convenient meeting point within large, city-scale outdoor environments. Each agent must then navigate safely while avoiding dynamic sentinels patrolling the area, using a tool that provides coarse spatial information. To address this, we propose CoSaR (Cooperative Spatial Reasoning and Planning), a framework that bridges the high-level communication and planning abilities of foundation models with the precision of classical spatial navigation algorithms. CoSaR enables agents to exchange situational updates, reason over evolving spatial constraints, and collaboratively replan trajectories. Evaluated across 14 city-level scenes with 3-5 agents, CoSaR consistently leads to faster gathering, shorter path lengths, and improved safety. Our results demonstrate that integrating dynamic communication with spatial reasoning is essential for robust multi-agent cooperation. By formalizing this new setting and providing a scalable benchmark, we aim to build a foundation for advancing cooperative spatial intelligence in embodied multi-agent systems. Code and challenge are available at https://github.com/UMass-Embodied-AGI/Sentinel.

Related papers

• Space Network of Experts: Architecture and Expert Placement [53.89437186709926]
  Space data centers are envisioned as a promising platform for executing energy-intensive large language models (LLMs)<n>One key challenge, however, is the efficient distributed deployment of a large-scale LLM in a satellite network.<n>We present the Space Network of Experts (Space-XNet) framework targeting the distributed execution of a popular mixture-of-experts (MoE) model in space.
  arXiv  Detail & Related papers  (2026-05-01T08:40:31Z)
• SynAgent: Generalizable Cooperative Humanoid Manipulation via Solo-to-Cooperative Agent Synergy [59.86199649493784]
  Controllable cooperative humanoid manipulation is a fundamental yet challenging problem for embodied intelligence.<n>We present SynAgent, a framework that enables scalable and physically plausible cooperative manipulation.<n>In experiments, SynAgent significantly outperforms existing baselines in both cooperative imitation and trajectory-conditioned control.
  arXiv  Detail & Related papers  (2026-04-20T17:46:20Z)
• CoEnv: Driving Embodied Multi-Agent Collaboration via Compositional Environment [54.84167973913561]
  CoEnv is a framework that leverages simulation for safe strategy exploration while ensuring reliable real-world deployment.<n>CoEnv operates through three stages: real-to-sim scene reconstruction that digitizes physical workspaces, VLM-driven action synthesis supporting both real-time planning with code-based trajectory generation, and validated sim-to-real transfer with collision detection for safe deployment.
  arXiv  Detail & Related papers  (2026-04-07T06:24:41Z)
• TeamHOI: Learning a Unified Policy for Cooperative Human-Object Interactions with Any Team Size [54.0714652192002]
  Physics-based humanoid control has achieved remarkable progress in enabling realistic and high-performing single-agent behaviors.<n>We present TeamHOI, a framework that enables a single decentralized policy to handle cooperative HOIs across any number of cooperating agents.
  arXiv  Detail & Related papers  (2026-03-09T05:52:13Z)
• Path Planning Optimisation for SParse, AwaRe and Cooperative Networked Aerial Robot Teams (SpArC-NARTs): Optimisation Tool and Ground Sensing Coverage Use Cases [1.376408511310322]
  A networked aerial robot team (NART) comprises a group of agents interconnected by wireless links.<n>Inter-agent connectivity, even if intermittent (i.e. sparse), enables data exchanges between agents and supports cooperative behaviours.<n>This paper proposes a novel path planning tool for a Sparse, Aware, and Cooperative Networked Aerial Robot Team.
  arXiv  Detail & Related papers  (2026-02-15T17:40:45Z)
• IMAGINE: Intelligent Multi-Agent Godot-based Indoor Networked Exploration [0.0]
  This paper implements Multi-Agent Reinforcement Learning (MARL) to address challenges in a 2D indoor environment.<n>Policy training aims to achieve emergent collaborative behaviours and decision-making under uncertainty.
  arXiv  Detail & Related papers  (2026-02-02T22:08:41Z)
• ORION: Option-Regularized Deep Reinforcement Learning for Cooperative Multi-Agent Online Navigation [5.834992848353096]
  ORION is a novel deep reinforcement learning framework for cooperative multi-agent online navigation in partially known environments.<n>At the core of ORION is an option-critic framework that learns to reason about a set of high-level cooperative modes.<n>Our simulation results show that ORION achieves high-quality, real-time decentralized cooperation over varying team sizes.
  arXiv  Detail & Related papers  (2026-01-03T10:55:10Z)
• Performance-Aware Self-Configurable Multi-Agent Networks: A Distributed Submodular Approach for Simultaneous Coordination and Network Design [3.5527561584422465]
  We present AlterNAting COordination and Network-Design Algorithm (Anaconda) Anaconda is a scalable algorithm that also enjoys near-optimality guarantees. We demonstrate in simulated scenarios of area monitoring and compare it with a state-of-the-art algorithm.
  arXiv  Detail & Related papers  (2024-09-02T18:11:33Z)
• AI planning in the imagination: High-level planning on learned abstract search spaces [68.75684174531962]
  We propose a new method, called PiZero, that gives an agent the ability to plan in an abstract search space that the agent learns during training. We evaluate our method on multiple domains, including the traveling salesman problem, Sokoban, 2048, the facility location problem, and Pacman.
  arXiv  Detail & Related papers  (2023-08-16T22:47:16Z)
• AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning [4.843554492319537]
  We propose an algorithm that allows robots to efficiently coordinate their strategies in the presence of adversarial inter-agent communications. It is assumed that the robots have no prior knowledge of the target locations, and they can interact with only a subset of neighboring robots at any time. The effectiveness of our approach is demonstrated on a collection of prototype grid-world environments.
  arXiv  Detail & Related papers  (2022-12-20T08:13:29Z)
• Learning to cooperate: Emergent communication in multi-agent navigation [49.11609702016523]
  We show that agents performing a cooperative navigation task learn an interpretable communication protocol. An analysis of the agents' policies reveals that emergent signals spatially cluster the state space. Using populations of agents, we show that the emergent protocol has basic compositional structure.
  arXiv  Detail & Related papers  (2020-04-02T16:03:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.