ROS-LLM：一个具有任务反馈和结构化推理功能的ROS框架，用于具身人工智能。

摘要

我们提出了一个框架，用于通过自然语言提示和来自机器人操作系统（ROS）的上下文信息，使非专家能够直观地对机器人进行编程。我们的系统集成了大型语言模型（LLMs），使非专家能够通过聊天界面向系统表达任务需求。该框架的关键特点包括：将ROS与连接到大量开源和商业LLMs的人工智能代理进行集成，从LLM输出中自动提取行为并执行ROS操作/服务，支持三种行为模式（顺序、行为树、状态机），模仿学习用于将新的机器人动作添加到可能动作库中，以及通过人类和环境反馈实现LLM反思。广泛的实验证实了该框架的鲁棒性、可扩展性和多功能性，展示了在不同场景下的长期任务、桌面重新布置和远程监督控制等方面的优势。为了促进我们框架的采用并支持我们结果的再现，我们已经将我们的代码开源。您可以在以下网址访问：https://github.com/huawei-noah/HEBO/tree/master/ROSLLM。

English

We present a framework for intuitive robot programming by non-experts, leveraging natural language prompts and contextual information from the Robot Operating System (ROS). Our system integrates large language models (LLMs), enabling non-experts to articulate task requirements to the system through a chat interface. Key features of the framework include: integration of ROS with an AI agent connected to a plethora of open-source and commercial LLMs, automatic extraction of a behavior from the LLM output and execution of ROS actions/services, support for three behavior modes (sequence, behavior tree, state machine), imitation learning for adding new robot actions to the library of possible actions, and LLM reflection via human and environment feedback. Extensive experiments validate the framework, showcasing robustness, scalability, and versatility in diverse scenarios, including long-horizon tasks, tabletop rearrangements, and remote supervisory control. To facilitate the adoption of our framework and support the reproduction of our results, we have made our code open-source. You can access it at: https://github.com/huawei-noah/HEBO/tree/master/ROSLLM.

ROS-LLM：一个具有任务反馈和结构化推理功能的ROS框架，用于具身人工智能。

ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning

摘要

Support