We present our general-purpose mobile manipulation system consisting of a custom robot platform and key algorithms spanning perception and planning. To extensively test the system in the wild and benchmark its performance, we choose a grocery shopping scenario in an actual, unmodified grocery store. We derive key performance metrics from detailed robot log data collected during six week-long field tests, spread across 18 months. These objective metrics, gained from complex yet repeatable tests, drive the direction of our research efforts and let us continuously improve our system's performance. We find that thorough end-to-end system-level testing of a complex mobile manipulation system can serve as a reality-check for state-of-the-art methods in robotics. This effectively grounds robotics research efforts in real world needs and challenges, which we deem highly useful for the advancement of the field. To this end, we share our key insights and takeaways to inspire and accelerate similar system-level research projects.

翻译：我们介绍了一种通用移动操作系统，包含自定义机器人平台及覆盖感知与规划的关键算法。为在野外环境中全面测试该系统并评估其性能，我们选择在实际未改造的杂货店中执行杂货购物场景任务。通过历时18个月、六次为期一周的野外测试所收集的详细机器人日志数据，我们推导出关键性能指标。这些源于复杂但可重复测试的客观指标，引导研究方向并持续提升系统性能。我们发现，对复杂移动操作系统的端到端系统级测试可作为机器人领域前沿方法的现实校验，有效将机器人研究工作锚定于真实世界需求与挑战，这对推动领域发展至关重要。为此，我们分享核心洞见与经验总结，以激发并加速类似系统级研究项目。