AEROS: A Single-Agent Operating Architecture with Embodied Capability Modules
Xue Qin, Simin Luan, Cong Yang, Zhijun Li
TLDR
AEROS is a new single-agent operating architecture for robots, using modular capability packages to achieve robust, safe, and extensible task execution.
Key contributions
- Models robots as single, persistent agents with capabilities extended via Embodied Capability Modules (ECMs).
- ECMs encapsulate skills, models, and tools, with a policy-separated runtime enforcing safety and constraints.
- Achieves 100% task success in simulations, outperforming baselines (92-93%) across diverse tasks.
- Enables modular extensibility, composable capability execution, and consistent system-level safety.
Why it matters
Robotic systems often lack a unified way to organize intelligence and capabilities. AEROS provides a principled abstraction, enabling robots to be more robust, safer, and easily extensible. This architecture could significantly advance the development of more capable and reliable autonomous agents.
Original Abstract
Robotic systems lack a principled abstraction for organizing intelligence, capabilities, and execution in a unified manner. Existing approaches either couple skills within monolithic architectures or decompose functionality into loosely coordinated modules or multiple agents, often without a coherent model of identity and control authority. We argue that a robot should be modeled as a single persistent intelligent subject whose capabilities are extended through installable packages. We formalize this view as AEROS (Agent Execution Runtime Operating System), in which each robot corresponds to one persistent agent and capabilities are provided through Embodied Capability Modules (ECMs). Each ECM encapsulates executable skills, models, and tools, while execution constraints and safety guarantees are enforced by a policy-separated runtime. This separation enables modular extensibility, composable capability execution, and consistent system-level safety. We evaluate a reference implementation in PyBullet simulation with a Franka Panda 7-DOF manipulator across eight experiments covering re-planning, failure recovery, policy enforcement, baseline comparison, cross-task generality, ECM hot-swapping, ablation, and failure boundary analysis. Over 100 randomized trials per condition, AEROS achieves 100% task success across three tasks versus baselines (BehaviorTree.CPP-style and ProgPrompt-style at 92--93%, flat pipeline at 67--73%), the policy layer blocks all invalid actions with zero false acceptances, runtime benefits generalize across tasks without task-specific tuning, and ECMs load at runtime with 100% post-swap success.
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