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Federated Single-Agent Robotics: Multi-Robot Coordination Without Intra-Robot Multi-Agent Fragmentation

arXiv 2604.11028 ↗First posted 13 Apr 2026cs.RO cs.AIcited by 0

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Abstract (arXiv)

As embodied robots move toward fleet-scale operation, multi-robot coordination is becoming a central systems challenge. Existing approaches often treat this as motivation for increasing internal multi-agent decomposition within each robot. We argue for a different principle: multi-robot coordination does not require intra-robot multi-agent fragmentation. Each robot should remain a single embodied agent with its own persistent runtime, local policy scope, capability state, and recovery authority, while coordination emerges through federation across robots at the fleet level. We present Federated Single-Agent Robotics (FSAR), a runtime architecture for multi-robot coordination built on single-agent robot runtimes. Each robot exposes a governed capability surface rather than an internally fragmented agent society. Fleet coordination is achieved through shared capability registries, cross-robot task delegation, policy-aware authority assignment, trust-scoped interaction, and layered recovery protocols. We formalize key coordination relations including authority delegation, inter-robot capability requests, local-versus-fleet recovery boundaries, and hierarchical human supervision, and describe a fleet runtime architecture supporting shared Embodied Capability Module (ECM) discovery, contract-aware cross-robot coordination, and fleet-level governance. We evaluate FSAR on representative multi-robot coordination scenarios against decomposition-heavy baselines. Results show statistically significant gains in governance locality (d=2.91, p<.001 vs. centralized control) and recovery containment (d=4.88, p<.001 vs. decomposition-heavy), while reducing authority conflicts and policy violations across all scenarios. Our results support the view that the path from embodied agents to embodied fleets is better served by federation across coherent robot runtimes than by fragmentation within them.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 99 references that are in the map. First posted on arXiv 13 Apr 2026.

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23 of 99 references.

References · 23

  1. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,348
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  3. 2022RT-1: Robotics Transformer for Real-World Control at Scalecited by 2,061
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  9. 2023SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Modelscited by 153
  10. 2017Behavior Trees in Robotics and AI: An Introductioncited by 107
  11. 2023Scalable Multi-Robot Collaboration with Large Language Models: Centralized or Decentralized Systems?cited by 106
  12. 2023ProAgent: Building Proactive Cooperative Agents with Large Language Modelscited by 98
11 more
  1. 2024COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Modelscited by 45
  2. 2025RoboOS: A Hierarchical Embodied Framework for Cross-Embodiment and Multi-Agent Collaborationcited by 28
  3. 2025Large Language Models for Multi-Robot Systems: A Surveycited by 27
  4. 2016The Robotarium: A remotely accessible swarm robotics research testbedcited by 24
  5. 2024EMOS: Embodiment-aware Heterogeneous Multi-robot Operating System with LLM Agentscited by 24
  6. 2023From the Desks of ROS Maintainers: A Survey of Modern & Capable Mobile Robotics Algorithms in the Robot Operating System 2cited by 11
  7. 2026AEROS: A Single-Agent Operating Architecture with Embodied Capability Modulescited by 7
  8. 2026Harnessing Embodied Agents: Runtime Governance for Policy-Constrained Executioncited by 7
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  10. 2026Evolving Skill Modules under a Fixed Planner: Versioning, Rollback, and Runtime Governance for Long-Lived Robot Systemscited by 4
  11. 2026ECM Contracts: Contract-Aware, Versioned, and Governable Capability Interfaces for Embodied Agentscited by 2

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