分层逐个抓取，让机械臂在杂乱环境中抓物成功率突破84.9%。
Pyramid-Monozone Synergistic Grasping Policy in Dense Clutter
- 构建金字塔式分层序列，逐层分离物体减少遮挡
- 仅在顶层采样抓取点，优先抓最上层物体避障
- 实测300种新物品7000次抓取，极端杂乱下成功率84.9%
在密集杂乱环境中抓取多样化新物体对机器人自动化构成重大挑战，主要源于遮挡问题。本文提出金字塔-单区协同抓取策略（PMSGP），有效应对抓取中的遮挡。首先设计金字塔序列策略（PSP），将杂乱场景中的物体按层次结构排列，逐层隔离，使抓取检测模型每次聚焦单一层次。随后提出单区采样策略（MSP），在顶层采样抓取候选点，确保每次抓取针对最上层物体，从而有效避开大部分遮挡。我们在包含300种新物体的密集杂乱场景中进行了超过7000次真实世界抓取实验，结果表明PMSGP显著优于七种先进抓取方法。更重要的是，在包含100种不同家用物品的极端杂乱场景中，PMSGP将抓取成功率提升至84.9%。据我们所知，此前无研究达到类似性能。所有抓取视频见：https://www.youtube.com/@chenghaoli4532/playlists。
原文摘要 · Abstract (English)
Grasping a diverse range of novel objects in dense clutter poses a great challenge to robotic automation mainly due to the occlusion problem. In this work, we propose the Pyramid-Monozone Synergistic Grasping Policy (PMSGP) that enables robots to effectively handle occlusions during grasping. Specifically, we initially construct the Pyramid Sequencing Policy (PSP) to sequence each object in cluttered scenes into a pyramid structure. By isolating objects layer-by-layer, the grasp detection model is allowed to focus on a single layer during each grasp. Then, we devise the Monozone Sampling Policy (MSP) to sample the grasp candidates in the top layer. Through this manner, each grasp targets the topmost object, thereby effectively avoiding most occlusions. We performed more than 7,000 real-world grasping in densely cluttered scenes with 300 novel objects, demonstrating that PMSGP significantly outperforms seven competitive grasping methods. More importantly, we tested the grasping performance of PMSGP in extremely cluttered scenes involving 100 different household goods, and found that PMSGP pushed the grasp success rate to 84.9\%. To the best of our knowledge, no previous work has demonstrated similar performance. All grasping videos are available at: https://www.youtube.com/@chenghaoli4532/playlists.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。