Precise Pick-and-Place using Score-Based Diffusion Networks
Journal
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Series/Report No.
IEEE International Conference on Intelligent Robots and Systems
Part Of
IEEE International Conference on Intelligent Robots and Systems
Start Page
7484
End Page
7491
ISSN
21530858
ISBN (of the container)
979-835037770-5
ISBN
[9798350377705]
Date Issued
2024-10-14
Author(s)
DOI
10.1109/IROS58592.2024.10801772
Abstract
In this paper, we propose a novel coarse-to-fine continuous pose diffusion method to enhance the precision of pick-and-place operations within robotic manipulation tasks. Leveraging the capabilities of diffusion networks, we facilitate the accurate perception of object poses. This accurate perception enhances both pick-and-place success rates and overall manipulation precision. Our methodology utilizes a top-down RGB image projected from an RGB-D camera and adopts a coarse-to-fine architecture. This architecture enables efficient learning of coarse and fine models. A distinguishing feature of our approach is its focus on continuous pose estimation, which enables more precise object manipulation, particularly concerning rotational angles. In addition, we employ pose and color augmentation techniques to enable effective training with limited data. Through extensive experiments in simulated and real-world scenarios, as well as an ablation study, we comprehensively evaluate our proposed methodology. Taken together, the findings validate its effectiveness in achieving high-precision pick-and-place tasks.
Event(s)
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2024
Publisher
IEEE
Type
conference paper