2025/11/06 by Fischer, Tobias, Vollprecht, Wolf, Zalmstra, Bas +8 · 1 citation
Computer Science · Decision Sciences · #Advanced Software Engineering Methodologies #Distributed systems and fault tolerance #FOS: Computer and information sciences #Robotics (cs.RO) #Scientific Computing and Data Management #Software Engineering (cs.SE)
paper · doi:10.48550/arxiv.2511.04827
openalex publication_date 2025/11/06 · openalex created_date 2025/11/11 · openalex updated_date 2026/07/28
The reproducibility crisis in scientific computing constrains robotics research. Existing studies reveal that up to 70% of robotics algorithms cannot be reproduced by independent teams, while many others fail to reach deployment because creating shareable software environments remains prohibitively complex. These challenges stem from fragmented, multi-language, and hardware-software toolchains that lead to dependency hell. We present Pixi, a unified package-management framework that addresses these issues by capturing exact dependency states in project-level lockfiles, ensuring bit-for-bit reproducibility across platforms. Its high-performance SAT solver achieves up to 10x faster dependency resolution than comparable tools, while integration of the conda-forge and PyPI ecosystems removes the need for multiple managers. Adopted in over 5,300 projects since 2023, Pixi reduces setup times from hours to minutes and lowers technical barriers for researchers worldwide. By enabling scalable, reproducible, collaborative research infrastructure, Pixi accelerates progress in robotics and AI.