# A RoboStack Tutorial: Using the Robot Operating System Alongside the Conda and Jupyter Data Science Ecosystems

arXiv: https://arxiv.org/abs/2104.12910 · HTML: https://mlsanity.com/paper/2104.12910/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2021.

## Built on by (most cited first, 3 of 3)

- [RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation](https://mlsanity.com/paper/2402.14623/) (2024)
- [Active Inverse Learning in Stackelberg Trajectory Games](https://mlsanity.com/paper/2308.08017/) (2023)
- [Racing With ROS 2 A Navigation System for an Autonomous Formula Student Race Car](https://mlsanity.com/paper/2311.14276/) (2023)

## Abstract (arXiv)

We argue that it is beneficial to tightly couple the widely-used Robot Operating System with Conda, a cross-platform, language-agnostic package manager, and Jupyter, a web-based interactive computational environment affording scientific computing. We provide new ROS packages for Conda, enabling the installation of ROS alongside data-science and machine-learning packages with ease. Multiple ROS versions (currently ROS1 Melodic and Noetic, as well as ROS2 Foxy and Galactic) can run simultaneously on one machine, with pre-compiled binaries available for Linux, Windows and OSX, and the ARM architecture (e.g. the Raspberry Pi and the new Apple Silicon). To deal with the large size of the ROS ecosystem, we significantly improved the speed of the Conda solver and build system by rewriting the crucial parts in C++. We further contribute a collection of JupyterLab extensions for ROS, including plugins for live plotting, debugging and robot control, as well as tight integration with Zethus, an RViz like visualization tool. Taken together, RoboStack combines the best of the data-science and robotics worlds to help researchers and developers to build custom solutions for their academic and industrial projects.