Pyramid-Monozone Synergistic Grasping Policy in Dense Clutter

TL;DR

This paper introduces the Pyramid-Monozone Synergistic Grasping Policy (PMSGP), a novel approach that improves robotic grasping success in dense clutter by effectively handling occlusions through layered sequencing and targeted sampling.

Contribution

The paper presents a new grasping policy combining pyramid sequencing and monozone sampling, achieving superior performance in cluttered environments compared to existing methods.

Findings

Over 7,000 real-world grasping tests demonstrate high success rates.

PMSGP achieves an 84.9% success rate in extremely cluttered scenes.

Outperforms seven competitive grasping methods.

Abstract

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.