Practical Insights on Grasp Strategies for Mobile Manipulation in the Wild
🤖 AI Summary
Mobile manipulation robots exhibit severely limited reliability in grasping diverse objects within real-world unstructured environments—such as grocery stores. To address this, we propose a generalizable grasping strategy design paradigm tailored for “in-the-wild” deployment and introduce the SHOPPER robotic platform. Our approach integrates multi-view RGB-D perception, joint geometric-semantic modeling, adaptive grasp pose generation, online feedback-driven policy refinement, and coordinated mobile base–manipulator control. We conduct the first large-scale, cross-category grasping field experiments—hundreds of trials—in an operational supermarket, systematically identifying critical failure modes including occlusion, container interference, object deformation, and pose drift. The proposed method significantly improves grasp success rate and cross-scene policy transferability. Furthermore, we establish a practical evaluation framework to assess robustness and generalization in open environments. This work provides a novel pathway toward robust, real-world deployment of mobile manipulation systems.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Develop reliable grasp strategies for mobile manipulators in unstructured environments
Design general grasp strategies for diverse items in real grocery stores
Analyze failure modes to identify key grasping challenges in robotics
Innovation
Methods, ideas, or system contributions that make the work stand out.
SHOPPER platform for reliable grasping
General grasp strategies for diverse items
Real-world grocery store deployment testing
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