rosa
Basic Information
ROSA is an AI-powered assistant and developer library that enables natural-language interaction with ROS1 and ROS2 robotics systems. It is built on the LangChain framework and provides a Python API (a ROSA class) that accepts an LLM instance to reason about and command ROS-based robots. The project targets robotics developers and researchers who want to query system state, inspect topics, and issue robot commands using conversational prompts rather than low-level ROS commands. The README includes quick-start instructions, system requirements (Python 3.9+ and ROS Noetic or higher), a pip-installable package name, and pointers to demos, model configuration guidance, and a project wiki for documentation and custom agent development.
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App Details
Features
Natural-language querying and invocation of ROS systems through a ROSA Python API that integrates with user-supplied LLMs. Compatibility with ROS1 and ROS2 environments and examples that show reasoning plus execution workflows. Ability to create custom agents by inheriting from the ROSA class or instantiating with custom parameters, and to add tools and custom prompts. Shipping demos include a TurtleSim demo runnable via Docker and filmed demos using Spot (NeBula-Spot) and an upcoming Nvidia IsaacSim extension. Project provides model configuration guidance, a changelog, contributing guidelines, licensing, and videos/papers evidencing capabilities.
Use Cases
ROSA makes robotics development more accessible by letting developers and operators describe inspection, debugging, and control tasks in natural language instead of hand-crafting ROS commands. It can enumerate ROS topics and relationships, reason about system state, and execute sequences in simulation or on hardware, which accelerates prototyping and troubleshooting. The framework encourages adaptation to specific robots and environments through custom agents, tools, and prompts, and provides demo workflows (TurtleSim, Spot, IsaacSim bridge) to validate behaviors. Documentation, a model-configuration wiki, and demonstration assets help teams adopt the agent for testing, teaching, and iterative robot control.
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