Mobile manipulation robots are continuously advancing, with their grasping capabilities rapidly progressing. However, there are still significant gaps preventing state-of-the-art mobile manipulators from widespread real-world deployments, including their ability to reliably grasp items in unstructured environments. To help bridge this gap, we developed SHOPPER, a mobile manipulation robot platform designed to push the boundaries of reliable and generalizable grasp strategies. We develop these grasp strategies and deploy them in a real-world grocery store -- an exceptionally challenging setting chosen for its vast diversity of manipulable items, fixtures, and layouts. In this work, we present our detailed approach to designing general grasp strategies towards picking any item in a real grocery store. Additionally, we provide an in-depth analysis of our latest real-world field test, discussing key findings related to fundamental failure modes over hundreds of distinct pick attempts. Through our detailed analysis, we aim to offer valuable practical insights and identify key grasping challenges, which can guide the robotics community towards pressing open problems in the field.
View on arXiv@article{huang2025_2504.12512,
title={ Practical Insights on Grasp Strategies for Mobile Manipulation in the Wild },
author={ Isabella Huang and Richard Cheng and Sangwoon Kim and Dan Kruse and Carolyn Matl and Lukas Kaul and JC Hancock and Shanmuga Harikumar and Mark Tjersland and James Borders and Dan Helmick },
journal={arXiv preprint arXiv:2504.12512},
year={ 2025 }
}