TetraGrip: Sensor-Driven Multi-Suction Reactive Object Manipulation in Cluttered Scenes
This addresses the challenge of reliable object manipulation in unstructured warehouse settings for robotics, though it appears incremental as it builds on existing vacuum gripper technology with added sensors and actuation.
The paper tackled the problem of robotic grasping in cluttered warehouse environments with occlusions and diverse objects, introducing TetraGrip, a multi-suction gripper with sensors and RL-based policy, which improved picking success by 22.86% in stacked scenarios compared to a single-suction gripper.
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/}.