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.
@article{arxiv.2401.01474,
title = {Demonstrating Mobile Manipulation in the Wild: A Metrics-Driven Approach},
author = {Max Bajracharya and James Borders and Richard Cheng and Dan Helmick and Lukas Kaul and Dan Kruse and John Leichty and Jeremy Ma and Carolyn Matl and Frank Michel and Chavdar Papazov and Josh Petersen and Krishna Shankar and Mark Tjersland},
journal= {arXiv preprint arXiv:2401.01474},
year = {2024}
}