Efficient Coordination with the System-Level Shared State: An Embodied-AI Native Modular Framework

TL;DR

This paper introduces ANCHOR, a modular framework for Embodied AI systems that explicitly manages shared state and feedback, improving robustness, scalability, and self-healing capabilities in real-world deployments.

Contribution

ANCHOR provides a novel system-level primitives framework that decouples shared state management from communication, enabling scalable, reliable, and self-healing Embodied AI systems.

Findings

Validated closed-loop feasibility in real workflows

Characterized latency under different payloads

Demonstrated automatic recovery after crashes

Abstract

As Embodied AI systems move from research prototypes to real world deployments, they tend to evolve rapidly while remaining reliable under workload changes and partial failures. In practice, many deployments are only partially decoupled: middleware moves messages, but shared context and feedback semantics are implicit, causing interface drift, cross-module interference, and brittle recovery at scale. We present ANCHOR, a modular framework that makes decoupling and robustness explicit system-level primitives. ANCHOR separates (i) Canonical Records, an evolvable contract for the standardized shared state, from (ii) a communication bus for many-to-many dissemination and feedback-oriented coordination, forming an inspectable end-to-end loop. We validate closed-loop feasibility on a de-identified workflow instantiation, characterize latency distributions under varying payload sizes and publish rates, and demonstrate automatic stream resumption after hard crashes and restarts even with shared-memory loss. Overall, ANCHOR turns ad-hoc integration glue into explicit contracts, enabling controlled degradation under load and self-healing recovery for scalable deployment of closed-loop AI systems.