|
JAIST Repository >
School of Information Science >
Articles >
Journal Articles >
Please use this identifier to cite or link to this item:
https://hdl.handle.net/10119/19975
|
| Title: | Monozone-Centric Instance Grasping Policy in Large-Scale Dense Clutter |
| Authors: | Li, Chenghao
Chong, Nak Young |
| Keywords: | robot grasping
grasp detection
class-agnostic segmentation
large-scale dense clutter
deep learning |
| Issue Date: | 2025-07-24 |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| Magazine name: | IEEE/ASME Transactions on Mechatronics |
| Start page: | 1 |
| End page: | 11 |
| DOI: | 10.1109/TMECH.2025.3587805 |
| Abstract: | Despite the impressive performance of existing vision-guided robot grasping methods in dense clutter, their reliance on a fixed view often results in incomplete object geometry in the view boundary and limits grasping in more challenging large-scale dense clutter. Moreover, analyzing all objects during grasping can detract from the reasoning for specific objects. This work proposes the Monozone-centric Instance Grasping Policy (MCIGP) to solve these problems. Specifically, the first part is the Monozone View Alignment (MVA), wherein we design the dynamic monozone that can align the camera view according to different objects during grasping, thereby alleviating view boundary effects and realizing grasping in large-scale dense clutter scenarios. Then, we devise the Instance-specific Grasp Detection (ISGD) to predict and optimize grasp candidates for one specific object within the monozone, ensuring an in-depth analysis of this object. We performed over 8,000 real-world grasping experiments in different cluttered scenarios with 300 novel objects, demonstrating that MCIGP significantly outperforms seven competitive grasping methods. Notably, in a largescale densely cluttered scene involving 100 different household goods, MCIGP pushed the grasp success rate to 84.9%. To the best of our knowledge, no previous work has demonstrated similar performance. The source code and all grasping videos are available here. |
| Rights: | This is the author's version of the work. Copyright (C) 2025 IEEE. IEEE/ASME Transactions on Mechatronics (Early Access). DOI: https://doi.org/10.1109/TMECH.2025.3587805. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| URI: | https://hdl.handle.net/10119/19975 |
| Material Type: | author |
| Appears in Collections: | b10-1. 雑誌掲載論文 (Journal Articles)
|
Files in This Item:
| File |
Description |
Size | Format |
|---|
| N-CHONG-I-0725.pdf | | 3298Kb | Adobe PDF | View/Open |
|
All items in DSpace are protected by copyright, with all rights reserved.
|
