ROScribe aims to simplify ROS package creation for robotics projects by leveraging Large Language Models (LLMs). It targets both beginners seeking to lower the ROS skill barrier and experienced engineers looking to accelerate development. The primary benefit is the automated generation of ROS workspaces and code from natural language descriptions.

How It Works

ROScribe employs a four-agent, multi-step approach to build ROS workspaces. The SpecAgent defines the project's ROS graph, identifying nodes and topics. The GenAgent then generates Python code for each node or pulls existing open-source implementations. The PackAgent creates essential package files (launch, CMakeLists.txt, package.xml, README.md) and integrates the generated code. Finally, the SupportAgent assists with debugging and project lifecycle issues. This agentic, modular design allows for focused development and iterative refinement of the robotic software stack.

Quick Start & Requirements

• Installation and usage details are available on the project's wiki: https://github.com/RoboCoachTechnologies/ROScribe/wiki
• Requires ROS (Robot Operating System).
• LLM access is implied for core functionality.

Highlighted Details

• Automated ROS package generation from natural language.
• Agentic multi-step approach for workspace construction.
• Supports integration of generated code with open-source ROS repositories.
• Includes a support agent for debugging and project lifecycle assistance.

Maintenance & Community

• Encourages contributions from the robotics community.
• Contact for business inquiries: robocoachtechnologies@gmail.com.

Licensing & Compatibility

• License not explicitly stated in the README.

Limitations & Caveats

The README does not specify the license, which may impact commercial use or closed-source integration. The effectiveness and accuracy of LLM-generated code are dependent on the underlying models and prompt tuning.