-- 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. T o 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. 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. I. INTRODUCTION Grasping and placing of a large diversity of novel items is a fundamental problem in mobile manipulation, necessary for robots to be useful in real-world settings like the home. Significant progress has been made over the past decade, showing mobile manipulators grasping a diversity of items in lab settings. However, many grasping works abstract away different parts of the robot stack, leading to assumptions that do not hold in the real-world (e.g. Furthermore, few works have (1) been able to make the jump to the real world, or (2) exhibited reliability close to necessary for real-world deployment. This is reflected in the dearth in widespread deployments of commercial mobile manipulators.