Ah sure, it was OutdooROS i.e. a web visualizer for outdoor ROS robots. Later renamed to Vizanti.
The two examples I cited in my appeal were MAVROS and ROS Mobile, and yeah turns out the former is actually maintained by OSRF and the latter was in breach of guidelines.
Thanks for letting me know. I still feel like it shouldn't be a problem to have a name like that, especially for an open source project.
I will double check.
Yeah I figured it would be cool as long as it's non-commercial, but I was wrong too. I guess they're kinda forced to protect the trademark by the established guidelines or they'll lose it, but they also don't want to discourage people from developing stuff so they're not super heavy handed about it.
Thank you. This is the main focus of our startup. So we will make this tool better and better. You can find more documentations and demos on our github.
Maybe today it feels like less of a platform, but we plan to make it a platform by integrating more components into it.
ROScribe is an AI-native robot software solution now; other than software generation (which is far more than code generation; it includes software specification, code retrieval from online repos, code retrieval from internal IPs, as well as code generation), we want to facilitate simulation (gazebo,...) and the actual robot build (order components online, etc.). That's why we called it platform. We are an early stage startup; hopefully our product gets better.
I appreciate your feedback.
Thank you.
One of main features of ROScribe (which is still under development) is the use of RAG (Retrieval Augmented Generation) for robot software generation. In RAG, the code, rather than being generated by the LLM, is pulled from ROS index by LLM (LLM keeps all documentations and meta data of ROS index in a vector data base to figure out what piece of code is most suitable for the task at hand.) We are working on this part and we plan to publish a conference paper on it.
So the ultimate solution is a mixture of codes pulled from online repositories (ROS index), internal data (companies own IPs), and the code that LLM generates. LLM is handling all of three segments, but only codes as minimum as needed.
That is cool, and especially useful since LLMs often get details like URLs to repositories wrong, so I could see how this system would increase the accuracy of the output.
ROScribe uses GPT to ask you about the details of your robotic project, draws the RQT graph, generate the code for all ROS nodes, and writes the launch file and installation scripts. Here is a demo on how to use the tool:
