外观
论文arXiv 2607.166363 个月前
PhyAgentOS：一种用于具身智能体的自演化操作系统，解耦认知规划与物理执行
原文链接：PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution（arXiv 2607.16636 · 发布日期：2026-07-18）
PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution
PhyAgentOS以会话运行时和文件化状态解耦认知规划与物理执行，并在游戏、仿真和真实机器人上验证。
核心要点
- 异构具身中，模型与规划器组合缺少统一执行抽象、共享状态和持续经验。
- 系统以会话为调度最小单元，提供调度、验证、记忆、基准和安全服务。
- State-as-a-File用Markdown与YAML呈现跨层状态，避免层间代码依赖。
- 在LIBERO、Calvin、RoboCasa365上，跨多个VLA模型取得提升。
验证范围与局限：验证范围包括游戏、仿真和真实机器人，覆盖19+种仿真与物理具身。
英文摘要 (Abstract)
Vision-language-action models, world models, and agentic planners each advance physical intelligence, yet their composition lacks a common execution abstraction, shared state, semantic verification, and persistent experience across heterogeneous embodiments. We present PhyAgentOS, a runtime foundation delivering scheduling, verification, memory, benchmarking, and safety as system-level services. Its Session-Centered Runtime treats a session, not an action, as the minimum unit of scheduling, compatibility preflight, supervised execution, evidence collection, and acceptance. To decouple cognition from physical execution, the cognition-physics boundary is a file system: the State-as-a-File protocol materializes cross-layer state as Markdown with YAML, yielding inspectable, versionable records without code dependencies between Agent and Runtime layers. These views form a unified cognitive state space aligning intent, capabilities, environment, execution, and experience. The SessionVerifier distinguishes execution termination from semantic task completion via evidence-grounded verdicts of success, failure, or replan. Verified outcomes are consolidated through epistemic memory into reusable knowledge and corrective lessons, closing a trial-and-error loop without retraining. Benchmarking reuses the deployment session and verification path, so results trace to real execution. Layered safety constrains both policy-driven and agent-driven execution: preflight, action bridges, SafetyGuard, heartbeat monitoring, and target-local constraints. Validation is progressive: games test cognitive planning, simulation adds dynamics and control, real robots add hardware noise, with the cognitive layer held constant. PhyAgentOS is benchmarked on Optimus-67, StarDojo, and DST-Dojo, validated on 19+ simulated and physical embodiments, and gains on LIBERO, Calvin, and RoboCasa365 across multiple VLA models.
作者：Yang Liu, Weixing Chen, Xinshuai Song, Tao Pu, Siwen Mo, Yongjie Bai, Zihao Chen, Qianran Sun, Liruo Zhong, Ying Shen
来源
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