ROSClaw: 一种用于代理机器人控制与交互的OpenClaw ROS 2框架
ROSClaw: An OpenClaw ROS 2 Framework for Agentic Robot Control and Interaction
AI总结：
ROSClaw通过整合OpenClaw代理运行时与ROS 2，实现任意基础模型与ROS-enabled机器人之间的交互，支持动态能力发现、多模态观察归一化、预执行动作验证和结构化审计日志，验证了执行层设计对任务完成与安全行为的关键影响。
摘要：
基础模型可以赋予机器人开放性推理、语言理解和适应性规划能力，但将模型连接到物理机器人需要定制化的集成，将感知、动作和安全耦合到单一模型和平台。我们提出了ROSClaw，一种模型无关的执行层，整合了OpenClaw代理运行时与ROS 2，使任意基础模型能够通过（i）动态能力发现与标准化能力注入、（ii）多模态观察归一化、（iii）在可配置的安全范围内进行预执行动作验证、（iv）结构化审计日志，对任意ROS-enabled机器人进行感知、推理和动作。更换模型后端或机器人平台是配置更改；工具方案、安全执行和溯源日志保持不变。我们在三个平台（轮式、四足、人形）上部署ROSClaw，使用四个基础模型后端。在这一受控的子系统下，模型在非策略动作提案率上表现出高达4.8倍的差异（前沿模型之间为3.4倍），并产生与相同命令不同的物理行为。通过与ROSA的跨框架平衡协议验证，执行层设计，而非仅仅是提示词，显著影响任务完成和安全行为，确立了ROSClaw作为实用的代理机器人基础设施和可重复的具身AI测量工具。
英文摘要：
Foundation models can endow robots with open-ended reasoning, language understanding, and adaptive planning, yet connecting a model to a physical robot today requires bespoke integration that couples perception, actuation, and safety to a single model and platform. We present ROSClaw, a model-agnostic executive layer that integrates the OpenClaw agent runtime with ROS 2, enabling any foundation model to perceive, reason about, and act on any ROS-enabled robot through (i) dynamic capability discovery with standardized affordance injection, (ii) multimodal observation normalization, (iii) pre-execution action validation within a configurable safety envelope, and (iv) structured audit logging. Swapping model backends or robot platforms is a configuration change; tool schemas, safety enforcement, and provenance logging remain invariant. We deploy ROSClaw on three platforms (wheeled, quadruped, humanoid) with four foundation-model backends. Under this controlled substrate, models exhibit up to 4.8 x differences in out-of-policy action proposal rates (3.4 x among frontier models alone) and produce qualitatively distinct physical behaviors from identical commands. A cross-framework parity protocol against ROSA confirms that executive-layer design, not just prompt wording, significantly affects both task completion and safety behavior, establishing ROSClaw as both practical agentic-robot infrastructure and a reproducible measurement instrument for embodied AI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
