四吸盘机械臂在杂乱货架中实现动态抓取，成功率提升22.86%。
TetraGrip: Sensor-Driven Multi-Suction Reactive Object Manipulation in Cluttered Scenes
- 四吸盘搭配传感器，实时反馈调整抓取动作。
- 堆叠物体抓取成功率比单吸盘高22.86%。
- 可解决遮挡与复杂场景抓取难题，适合仓储机器人。
配备真空夹持器的仓库机器人需从密集货架中可靠抓取各类物品。然而，这类环境存在遮挡、物体朝向多样、堆叠遮挡及难吸附表面等挑战。本文提出TetraGrip，一种基于四吸盘的新型抓取策略，每个吸盘安装于线性执行器上，并配备光学飞行时间（ToF）近距传感器，实现反应式抓取。我们在仓库场景中评估该系统，验证其对堆叠与遮挡物体的操控能力。结果表明，基于强化学习的策略在堆叠物体场景中相比单吸盘抓取成功率达22.86%提升。此外，当单吸盘因物理限制失效时，TetraGrip仍能成功抓取：(1) 被遮挡的物体；(2) 复杂构型下的物体。这些发现凸显了多执行器真空抓取在非结构化仓库环境中的优势。项目网站：https://tetragrip.github.io/
原文摘要 · Abstract (English)
Warehouse robotic systems equipped with vacuum grippers must reliably grasp a diverse range of objects from densely packed shelves. However, these environments present significant challenges, including occlusions, diverse object orientations, stacked and obstructed items, and surfaces that are difficult to suction. We introduce \tetra, a novel vacuum-based grasping strategy featuring four suction cups mounted on linear actuators. Each actuator is equipped with an optical time-of-flight (ToF) proximity sensor, enabling reactive grasping. We evaluate \tetra in a warehouse-style setting, demonstrating its ability to manipulate objects in stacked and obstructed configurations. Our results show that our RL-based policy improves picking success in stacked-object scenarios by 22.86\% compared to a single-suction gripper. Additionally, we demonstrate that TetraGrip can successfully grasp objects in scenarios where a single-suction gripper fails due to physical limitations, specifically in two cases: (1) picking an object occluded by another object and (2) retrieving an object in a complex scenario. These findings highlight the advantages of multi-actuated, suction-based grasping in unstructured warehouse environments. The project website is available at: \href{https://tetragrip.github.io/}{https://tetragrip.github.io/}.
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