# ROSClaw: An OpenClaw ROS 2 Framework for Agentic Robot Control and Interaction

arXiv: https://arxiv.org/abs/2603.26997 · HTML: https://mlsanity.com/paper/2603.26997/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 19 of its 27 references that are in the map.
- First posted on arXiv 27 Mar 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 19 of 19)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Inner Monologue: Embodied Reasoning through Planning with Language Models](https://mlsanity.com/paper/2207.05608/) (2022)
- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://mlsanity.com/paper/2201.07207/) (2022)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [ChatGPT for Robotics: Design Principles and Model Abilities](https://mlsanity.com/paper/2306.17582/) (2023)
- [SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning](https://mlsanity.com/paper/2307.06135/) (2023)
- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [Behavior Trees in Robotics and AI: An Introduction](https://mlsanity.com/paper/1709.00084/) (2017)
- [GPT-4V(ision) for Robotics: Multimodal Task Planning from Human Demonstration](https://mlsanity.com/paper/2311.12015/) (2023)
- [ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning](https://mlsanity.com/paper/2406.19741/) (2024)
- [Enabling Novel Mission Operations and Interactions with ROSA: The Robot Operating System Agent](https://mlsanity.com/paper/2410.06472/) (2024)
- [Agentic AI for Robot Control: Flexible but still Fragile](https://mlsanity.com/paper/2602.13081/) (2026)

## Built on by (most cited first, 3 of 3)

- [PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution](https://mlsanity.com/paper/2607.16636/) (2026)
- [Octopus Protocol: One-Shot Hardware Discovery and Control for AI Agents via Infrastructure-as-Prompts](https://mlsanity.com/paper/2605.09055/) (2026)
- [MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention](https://mlsanity.com/paper/2606.01063/) (2026)

## Abstract (arXiv)

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.