
ros2ai
ros2ai is a next-generation ROS 2 command line interface extension with AI such as OpenAI
Stars: 109

ros2ai is a next-generation ROS 2 command line interface extension with OpenAI. It allows users to ask questions about ROS 2, get answers, and execute commands using natural language. ros2ai is easy to use, especially for ROS 2 beginners and students who do not really know ros2cli. It supports multiple languages and is available as a Docker container or can be built from source.
README:
ros2ai is a next-generation ROS 2 command line interface extension with OpenAI
see overview slide deck for more information.
- (Just for fun 😝)
- Getting answers against the questions directly without browsing, clicking and typing many times.
- Easy to use for everyone, especially for ROS 2 beginners and students who do not really know ros2cli.
- Multiple language support.
See how it works 🔥
https://github.com/fujitatomoya/ros2ai/assets/43395114/78a0799b-40e3-4dc8-99cb-488994e94769
Supported ROS Distribution
Distribution | Supported | Note |
---|---|---|
Rolling Ridley | ✅ | Development / Mainstream Branch |
Iron Irwini | ✅ | |
Humble Hawksbill | ✅ |
[!NOTE] Verified on Ubuntu 22.04 Jammy Jellyfish only, other platform would also work.
see available images for tomoyafujita/ros2ai@dockerhub
docker run -it --rm -e OPENAI_API_KEY=$OPENAI_API_KEY tomoyafujita/ros2ai:humble
[!NOTE]
OPENAI_API_KEY
environmental variable must be set
https://github.com/fujitatomoya/ros2ai/assets/43395114/2af4fd44-2ccf-472c-9153-c3c19987dc96
pip install openai
No released package is available, needs to be build in colcon workspace.
source /opt/ros/rolling/setup.bash
mkdir -p colcon_ws/src
cd colcon_ws/src
git clone https://github.com/fujitatomoya/ros2ai.git
cd ..
colcon build --symlink-install --packages-select ros2ai
-
ros2ai
requires OpenAI API key
export OPENAI_API_KEY='your-api-key-here'
[!CAUTION] Do not share or expose your OpenAI API key.
environmental variable | default | Note |
---|---|---|
OPENAI_MODEL_NAME | 'gpt-4' | AI model to be used. |
OPENAI_ENDPOINT | 'https://api.openai.com/v1' | API endpoint URL. |
OPENAI_TEMPERATURE | 0.5 | OpenAI temperature |
[!NOTE] These are optional environmental variables. if not set, default value will be used.
-
status
to check OpenAI API key is valid.
root@tomoyafujita:~/docker_ws/ros2_colcon# ros2 ai status -v
----- api_model: gpt-4
----- api_endpoint: https://api.openai.com/v1
----- api_token: None
As an artificial intelligence, I do not have a physical presence, so I can't be "in service" in the traditional sense. But I am available to assist you 24/7.
[SUCCESS] Valid OpenAI API key.
-
query
to ask any questions to ROS 2 assistant.
root@tomoyafujita:~/docker_ws/ros2_colcon# ros2 ai query "Tell me how to check the available topics?"
To check the available topics in ROS 2, you can use the following command in the terminal:
---
ros2 topic list
---
After you enter this command, a list of all currently active topics in your ROS2 system will be displayed. This list includes all topics that nodes in your system are currently publishing to or subscribing from.
-
exec
that ROS 2 assistant can execute the appropriate command based on your request.
root@tomoyafujita:~/docker_ws/ros2_colcon# ros2 ai exec "give me all nodes"
/talker
root@tomoyafujita:~/docker_ws/ros2_colcon# ros2 ai exec "what topics available"
/chatter
/parameter_events
/rosout
root@tomoyafujita:~/docker_ws/ros2_colcon# ros2 ai exec "give me detailed info for topic /chatter"
Type: std_msgs/msg/String
Publisher count: 1
Subscription count: 0
- Japanese (could be any language ❓)
root@tomoyafujita:~/docker_ws/ros2_colcon# ros2 ai query "パラメータのリスト取得方法を教えて"
ROS 2のパラメータリストを取得するには、コマンドラインインターフェース(CLI)を使います。具体的には、次のコマンドを使用します:
---code
ros2 param list
---
このコマンドは、現在動作しているすべてのノードのパラメーターをリストアップします。特定のノードのパラメータだけを見たい場合には、以下のようにノード名を指定することもできます。
---code
ros2 param list /node_name
---
このようにして、ROS2のパラメータリストの取得を行うことが可能です。なお、上述したコマンドはシェルから直接実行してください。
Special thanks to OpenAI API 🌟🌟🌟
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for ros2ai
Similar Open Source Tools

ros2ai
ros2ai is a next-generation ROS 2 command line interface extension with OpenAI. It allows users to ask questions about ROS 2, get answers, and execute commands using natural language. ros2ai is easy to use, especially for ROS 2 beginners and students who do not really know ros2cli. It supports multiple languages and is available as a Docker container or can be built from source.

coreply
Coreply is an open-source Android app that provides texting suggestions while typing, enhancing the typing experience with intelligent, context-aware suggestions. It supports various texting apps and offers real-time AI suggestions, customizable LLM settings, and ensures no data collection. Users can install the app, configure it with an API key, and start receiving suggestions while typing in messaging apps. The tool supports different AI models from providers like OpenAI, Google AI Studio, Openrouter, Groq, and Codestral for chat completion and fill-in-the-middle tasks.

comp
Comp AI is an open-source compliance automation platform designed to assist companies in achieving compliance with standards like SOC 2, ISO 27001, and GDPR. It transforms compliance into an engineering problem solved through code, automating evidence collection, policy management, and control implementation while maintaining data and infrastructure control.

MockingBird
MockingBird is a toolbox designed for Mandarin speech synthesis using PyTorch. It supports multiple datasets such as aidatatang_200zh, magicdata, aishell3, and data_aishell. The toolbox can run on Windows, Linux, and M1 MacOS, providing easy and effective speech synthesis with pretrained encoder/vocoder models. It is webserver ready for remote calling. Users can train their own models or use existing ones for the encoder, synthesizer, and vocoder. The toolbox offers a demo video and detailed setup instructions for installation and model training.

deep-chat
Deep Chat is a fully customizable AI chat component that can be injected into your website with minimal to no effort. Whether you want to create a chatbot that leverages popular APIs such as ChatGPT or connect to your own custom service, this component can do it all! Explore deepchat.dev to view all of the available features, how to use them, examples and more!

ai-elements
AI Elements is a component library built on top of shadcn/ui to help build AI-native applications faster. It provides pre-built, customizable React components specifically designed for AI applications, including conversations, messages, code blocks, reasoning displays, and more. The CLI makes it easy to add these components to your Next.js project.

tunacode
TunaCode CLI is an AI-powered coding assistant that provides a command-line interface for developers to enhance their coding experience. It offers features like model selection, parallel execution for faster file operations, and various commands for code management. The tool aims to improve coding efficiency and provide a seamless coding environment for developers.

wa_llm
WhatsApp Group Summary Bot is an AI-powered tool that joins WhatsApp groups, tracks conversations, and generates intelligent summaries. It features automated group chat responses, LLM-based conversation summaries, knowledge base integration, persistent message history with PostgreSQL, support for multiple message types, group management, and a REST API with Swagger docs. Prerequisites include Docker, Python 3.12+, PostgreSQL with pgvector extension, Voyage AI API key, and a WhatsApp account for the bot. The tool can be quickly set up by cloning the repository, configuring environment variables, starting services, and connecting devices. It offers API usage for loading new knowledge base topics and generating & dispatching summaries to managed groups. The project architecture includes FastAPI backend, WhatsApp Web API client, PostgreSQL database with vector storage, and AI-powered message processing.

WeKnora
WeKnora is a document understanding and semantic retrieval framework based on large language models (LLM), designed specifically for scenarios with complex structures and heterogeneous content. The framework adopts a modular architecture, integrating multimodal preprocessing, semantic vector indexing, intelligent recall, and large model generation reasoning to build an efficient and controllable document question-answering process. The core retrieval process is based on the RAG (Retrieval-Augmented Generation) mechanism, combining context-relevant segments with language models to achieve higher-quality semantic answers. It supports various document formats, intelligent inference, flexible extension, efficient retrieval, ease of use, and security and control. Suitable for enterprise knowledge management, scientific literature analysis, product technical support, legal compliance review, and medical knowledge assistance.

paperless-gpt
paperless-gpt is a tool designed to generate accurate and meaningful document titles and tags for paperless-ngx using Large Language Models (LLMs). It supports multiple LLM providers, including OpenAI and Ollama. With paperless-gpt, you can streamline your document management by automatically suggesting appropriate titles and tags based on the content of your scanned documents. The tool offers features like multiple LLM support, customizable prompts, easy integration with paperless-ngx, user-friendly interface for reviewing and applying suggestions, dockerized deployment, automatic document processing, and an experimental OCR feature.

mcp-apache-spark-history-server
The MCP Server for Apache Spark History Server is a tool that connects AI agents to Apache Spark History Server for intelligent job analysis and performance monitoring. It enables AI agents to analyze job performance, identify bottlenecks, and provide insights from Spark History Server data. The server bridges AI agents with existing Apache Spark infrastructure, allowing users to query job details, analyze performance metrics, compare multiple jobs, investigate failures, and generate insights from historical execution data.

ps-fuzz
The Prompt Fuzzer is an open-source tool that helps you assess the security of your GenAI application's system prompt against various dynamic LLM-based attacks. It provides a security evaluation based on the outcome of these attack simulations, enabling you to strengthen your system prompt as needed. The Prompt Fuzzer dynamically tailors its tests to your application's unique configuration and domain. The Fuzzer also includes a Playground chat interface, giving you the chance to iteratively improve your system prompt, hardening it against a wide spectrum of generative AI attacks.

aicommit2
AICommit2 is a Reactive CLI tool that streamlines interactions with various AI providers such as OpenAI, Anthropic Claude, Gemini, Mistral AI, Cohere, and unofficial providers like Huggingface and Clova X. Users can request multiple AI simultaneously to generate git commit messages without waiting for all AI responses. The tool runs 'git diff' to grab code changes, sends them to configured AI, and returns the AI-generated commit message. Users can set API keys or Cookies for different providers and configure options like locale, generate number of messages, commit type, proxy, timeout, max-length, and more. AICommit2 can be used both locally with Ollama and remotely with supported providers, offering flexibility and efficiency in generating commit messages.

r2ai
r2ai is a tool designed to run a language model locally without internet access. It can be used to entertain users or assist in answering questions related to radare2 or reverse engineering. The tool allows users to prompt the language model, index large codebases, slurp file contents, embed the output of an r2 command, define different system-level assistant roles, set environment variables, and more. It is accessible as an r2lang-python plugin and can be scripted from various languages. Users can use different models, adjust query templates dynamically, load multiple models, and make them communicate with each other.

openlit
OpenLIT is an OpenTelemetry-native GenAI and LLM Application Observability tool. It's designed to make the integration process of observability into GenAI projects as easy as pie – literally, with just **a single line of code**. Whether you're working with popular LLM Libraries such as OpenAI and HuggingFace or leveraging vector databases like ChromaDB, OpenLIT ensures your applications are monitored seamlessly, providing critical insights to improve performance and reliability.

OpenSpec
OpenSpec is a tool for spec-driven development, aligning humans and AI coding assistants to agree on what to build before any code is written. It adds a lightweight specification workflow that ensures deterministic, reviewable outputs without the need for API keys. With OpenSpec, stakeholders can draft change proposals, review and align with AI assistants, implement tasks based on agreed specs, and archive completed changes for merging back into the source-of-truth specs. It works seamlessly with existing AI tools, offering shared visibility into proposed, active, or archived work.
For similar tasks

ros2ai
ros2ai is a next-generation ROS 2 command line interface extension with OpenAI. It allows users to ask questions about ROS 2, get answers, and execute commands using natural language. ros2ai is easy to use, especially for ROS 2 beginners and students who do not really know ros2cli. It supports multiple languages and is available as a Docker container or can be built from source.
For similar jobs

weave
Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack
LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

VisionCraft
The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

kaito
Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT
PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

tabby
Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

spear
SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick
Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.