As a long-standing challenge in robotic manipulation, stable and efficient grasping in cluttered environments is of great importance in industrial settings. While recent studies have achieved relatively high success rates in grasping from clutter, there remain few mature solutions for more demanding tasks such as sequential object search and sorting. This work addresses sequential object picking in cluttered environments based on the Cluttered Environment Picking Benchmark (CEPB) and presents our solution to the Pick-in-Clutter track of the 10th Robotic Grasping and Manipulation Competition (RGMC) at ICRA 2025. The task poses several key challenges. First, it requires robust and collision-aware grasping with high success rates across a diverse set of objects, including both rigid and deformable ones. Second, it demands efficient search for target objects, which places stringent requirements on the decluttering and searching strategies of the solution. To address the above challenges, we design an integrated hardware-software pipeline that combines object recognition, decluttering, and multi-modal grasping. The main contributions include the hardware design of a multifunctional gripper and novel representations for object distribution and occlusion relationships in cluttered space. This pipeline enables efficient recognition, search, and sequential grasping of objects in clutter, demonstrating strong performance in both laboratory tests and competition scenarios, and ultimately achieving second place in the Pick-in-Clutter track of the RGMC 2025.

翻译：作为机器人操作领域的长期挑战，在杂乱环境中实现稳定高效的抓取在工业场景中具有重要价值。尽管近年研究在杂乱环境抓取中已取得较高成功率，但仍缺乏针对序列化物体搜索与分拣等复杂任务的成熟方案。本研究基于杂乱环境抓取基准（CEPB）聚焦有序物体抓取问题，提出面向第10届国际机器人抓取与操作竞赛（RGMC）杂乱抓取赛道（ICRA 2025）的解决方案。该任务面临多项关键挑战：首先需要在包含刚性和柔性物体的多样化对象集上实现高成功率的鲁棒碰撞感知抓取；其次要求对目标物体进行高效搜索，这对解决方案的整理与搜索策略提出严苛要求。为应对上述挑战，我们设计了集成物体识别、整理与多模态抓取的硬件-软件一体化流水线。主要贡献包括多功能夹爪的硬件设计，以及杂乱空间中物体分布与遮挡关系的新型表征方法。该流水线实现了杂乱环境中的高效识别、搜索与序列化物体抓取，在实验室测试与竞赛场景中均展现出强劲性能，最终荣获2025年RGMC杂乱抓取赛道亚军。