在真实超市中测试并总结移动机械臂抓取的实用经验。
Practical Insights on Grasp Strategies for Mobile Manipulation in the Wild
- 设计通用抓取策略，适配复杂多变的超市环境。
- 实测数百次抓取，发现关键失败模式。
- 为机器人抓取研究提供可落地的实践指南。
移动操作机器人正快速进步，其抓取能力持续提升。然而，最先进的移动操作机器人仍难以在真实世界广泛部署，主要受限于其在非结构化环境中可靠抓取物体的能力。为缩小这一差距，我们开发了SHOPPER移动操作机器人平台，旨在推动可靠且可泛化的抓取策略边界。我们在一个真实的杂货店中部署并测试这些抓取策略——该场景因物品、货架和布局的高度多样性而极具挑战性。本文详细阐述了针对在真实杂货店中任意抓取物品的通用抓取策略设计方法，并深入分析了最新一次实地测试结果，讨论了数百次不同抓取尝试中的关键失败模式。通过详尽分析，我们旨在提供有价值的实践经验，识别核心抓取挑战，引导机器人领域聚焦当前亟待解决的关键问题。
原文摘要 · Abstract (English)
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.
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