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

Abstract: 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.

• The paper proposes a federated approach to maintain robots as coherent single agents, avoiding internal multi-agent fragmentation in multi-robot systems.
• It details a formal model incorporating an ECM registry, cross-robot delegation protocol, policy resolver, recovery orchestrator, and hierarchical human oversight.
• Empirical evaluation shows significant improvements in governance locality, audit trail completeness, recovery containment, and reduction of authority conflicts.

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

Introduction and Motivation

The transition from isolated robotic deployments toward coordinated multi-robot fleets introduces complex systems challenges, primarily attributed to coordination, governance, and recovery in heterogeneous, distributed environments. Traditional approaches to multi-robot systems have advocated for increasing internal agent decomposition within each robot, leading to architectures where robots are modeled as collections of internally fragmented agents interacting with other similar collections. This paper proposes a fundamental alternative: Federated Single-Agent Robotics (FSAR), which posits that each robot should remain a single, coherent embodied agent, maintaining its local runtime, policy domain, and authority, while fleet-scale coordination is facilitated externally via federated mechanisms.

FSAR's central thesis is that multi-robot coordination does not logically necessitate intra-robot multi-agent fragmentation. Rather, clarity and coherence in coordination, audit, policy enforcement, and recovery can be achieved more effectively by federating at the fleet level, obviating the systems complexity and governance ambiguity arising from the proliferation of internal agent boundaries within each robot.

FSAR Formal Model

In FSAR, individual robots are treated as singleton agents, each defined by a runtime tuple capturing agent identity, capability set (ECMs), local policy, trust context, recovery authorities, and human oversight interface. The fleet is formalized as a collection of such runtimes, coordinated via an explicit federation layer providing the following abstractions:

• Shared ECM Registry ($\Gamma$): Publicly advertised, type- and contract-aware registry of exposed capabilities (ECMs).
• Cross-Robot Delegation Protocol ($\Delta$): Policy- and trust-aware inter-robot request and delegation system.
• Policy Resolver ($\Pi$): Mechanism for composing and resolving local and cross-robot policy domains.
• Recovery Orchestrator ($\Omega$): Layered, monotonic recovery escalation from local to fleet to human supervision.
• Hierarchical Human Oversight ($\Lambda$): Coordinated interfacing for local and fleet-level human supervisory involvement.

Key coordination relations (e.g., capability requests, authority delegation, layered recovery, and supervision) are made explicit and governed by model invariants, maintaining clear boundaries for agency, attribution, and authority.

Architecture and Component Semantics

The proposed architecture separates the system into (1) local robot runtimes and (2) the federation layer. Within each robot, the runtime orchestrates internal software modules but preserves a single externally visible agent boundary. Cross-robot coordination is achieved via registry-based discovery, trust-aware and policy-composed delegation requests, and robust audit mechanisms. Notably, FSAR ensures that the authority to execute or recover remains aligned with the robot's local policy and state, with no cross-overwrite by remote coordination or fleet-level decisions unless explicitly governed by federation-layer trust and policy contracts.

The registry distinguishes capability possession, advertisement, and delegability, thus preventing discoverability from being erroneously conflated with immediate availability or authority. Every cross-robot interaction is subject to local admissibility, non-transitive delegation, revocable authority, and strictly governed trust scopes.

Evaluation Methodology

The evaluation leverages a protocol-level simulator implementing FSAR, as well as two baselines: centralized fleet control (CFC) and decomposition-heavy multi-agent (DHMA). The evaluation spans five representative scenarios (e.g., door relay, collaborative delivery, layered recovery, trust-constrained inspection, resource contention), three fleet sizes (up to 16 robots), and includes ablation studies to isolate the roles of trust, policy, recovery, and registry mechanisms.

Eight metrics are instrumented: task success, governance locality, recovery containment, authority conflicts, policy violations, reassignment latency, human intervention frequency, and audit attributability. All architectures share the same capability matching logic to isolate the impact of coordination and governance topology.

Experimental Results

Key empirical findings:

• Governance and Auditability: FSAR achieves strong gains in governance locality (0.94 vs 0.69/0.53 for CFC/DHMA) and audit trail completeness (0.98 vs 0.84/0.53), as every principal is uniquely attributable without traversing nested agent chains or central controllers.
• Recovery Containment: FSAR contains 89% of failures at the local or peer level (vs 51% CFC and 80% DHMA), reflecting the effectiveness of monotonic, layered recovery escalation.
• Conflict and Policy Violation: FSAR reduces authority conflicts and policy violations (1.04 and 0.4 per run, respectively, vs. 3.79/8.11 and 1.66/3.34 for CFC/DHMA), indicating clearer authority boundaries and more robust policy composition.
• Scalability: FSAR's governance metrics degrade minimally with fleet scaling, in contrast to CFC and DHMA, which suffer from centralized attribution bottlenecks and quadratic growth in inter-agent conflicts.
• Ablation: Removal of the ECM registry or layered recovery strongly degrades success and containment; omitting policy or trust mechanisms increases conflicts and violations, confirming the criticality of federation-layer governance components.

Statistical testing indicates these governance metrics show large effect sizes ($d > 2$ in most cases) against both alternatives.

Theoretical and Practical Implications

The FSAR architecture delivers a contrapuntal systems claim to the established MAS and MRTA traditions: the locus of coordination complexity should be at the inter-robot federation layer, not internal agent proliferation within each robot. This design sharply constrains coordination overhead, recovers local coherence, and supports precise policy, recovery, and audit semantics at scale.

Practical implications: FSAR's auditability and governance make it highly suitable for regulated, safety-critical, or enterprise contexts where explanation, local recovery, and human supervision are mandatory. Centralized or decomposition-heavy approaches become untenable for audit locality, escalation, or bounded delegation, particularly as the fleet grows or becomes more mission-diverse. By ensuring that only the robot itself owns execution and local recovery, FSAR lays the groundwork for robust physical safety and formal runtime assurance in multi-robot systems.

Limits and Open Directions: FSAR admits its model is less suited to tightly-coupled manipulation or scenarios favoring global optimization via centralized or coalition-based planning. Hybrid architectures, which allow for local agent concurrency inside robots while maintaining single-agent boundaries externally, form a necessary extension in such domains. Furthermore, FSAR's policy and trust layers, though strong in runtime isolation and audit semantics, may require further evolution to address dynamic coalition formation, emergent behaviors, or larger-scale market-based task allocation.

Conclusion

Federated Single-Agent Robotics formally demonstrates that cross-robot coordination and governance can be operationalized without internal multi-agent fragmentation. Empirical and architectural evidence shows that the federated model retains or improves coordination efficacy, while delivering sharply improved governance locality, authority containment, and auditability—principles foundational for future embodied fleet operating systems and safety-critical robotics deployments. Extending the federation paradigm—while relaxing the single-agent assumption where local parallelism or tight coupling is indispensable—remains an important avenue for advancing fleet-scale embodied AI.