让任意大模型轻松控制不同机器人，实现安全可靠的智能交互。
ROSClaw: An OpenClaw ROS 2 Framework for Agentic Robot Control and Interaction
- 构建模型无关的执行层，动态发现机器人能力并标准化注入
- 在三种平台四类模型下，行为差异达4.8倍，验证了架构影响
- 适合研究具身智能、机器人控制与安全评估的开发者和学者
基础模型可赋予机器人开放式的推理、语言理解和自适应规划能力，但当前将模型与物理机器人连接需定制化集成，耦合感知、执行与安全机制。我们提出ROSClaw，一个与模型无关的执行层，将OpenClaw代理运行时与ROS 2集成，使任意基础模型可通过（i）动态能力发现与标准化可操作性注入，（ii）多模态观测归一化，（iii）可配置安全范围内的预执行动作验证，（iv）结构化审计日志，实现对任意ROS兼容机器人的感知、推理与行动。更换模型或机器人仅需配置变更；工具模式、安全策略与溯源日志保持不变。我们在轮式、四足与人形三类平台上部署，使用四类基础模型。在此统一环境中，模型在越轨动作提议率上最高相差4.8倍（前沿模型间相差3.4倍），相同指令下产生明显不同的物理行为。与ROSA对比的跨框架一致性协议表明，执行层设计本身显著影响任务完成率与安全行为，确立ROSClaw既是实用的智能机器人基础设施，也是可复现的具身AI评测工具。
原文摘要 · Abstract (English)
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.
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