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.
翻译:科学计算中的可复现性危机制约了机器人学研究的进展。现有研究表明,高达70%的机器人学算法无法被独立团队复现,而更多算法因构建可共享的软件环境仍极其复杂而未能实现部署。这些挑战源于碎片化、多语言及软硬件工具链割裂所导致的依赖关系困境。本文提出Pixi——一个统一的包管理框架,通过项目级锁文件记录精确的依赖状态,确保跨平台的比特级可复现性,从而解决上述问题。其高性能SAT求解器实现比同类工具快10倍的依赖解析速度,同时集成conda-forge与PyPI生态系统,消除了多包管理器的需求。自2023年起已在5300余个项目中投入使用,Pixi将环境搭建时间从数小时缩短至数分钟,降低了全球研究者的技术门槛。通过构建可扩展、可复现、可协作的研究基础设施,Pixi正加速机器人学与人工智能领域的进步。