Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching
Name
0278364919868017.pdf
Description
Published version
Size
3.03 MB
Format
Adobe PDF
Checksum (MD5)
05efbaa2c7a3ba78b306a6f7fe461905
Author(s)
Zeng, Andy
Song, Shuran
Yu, Kuan-Ting
Donlon, Elliott S
Hogan, Francois R.
Bauza Villalonga, Maria
Ma, Daolin
Taylor, Orion Thomas
Liu, Melody
Romo, Eudald
Date Issued
August 2019
Journal
International Journal of Robotics Research
Publisher
SAGE Publications
Citation
Zeng, Andy et al. "Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching." International Journal of Robotics Research (August 2019): 1-16.
Version
Final published version
Abstract
This article presents a robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. The key new feature of the system is that it handles a wide range of object categories without needing any task-specific training data for novel objects. To achieve this, it first uses an object-agnostic grasping framework to map from visual observations to actions: inferring dense pixel-wise probability maps of the affordances for four different grasping primitive actions. It then executes the action with the highest affordance and recognizes picked objects with a cross-domain image classification framework that matches observed images to product images. Since product images are readily available for a wide range of objects (e.g., from the web), the system works out-of-the-box for novel objects without requiring any additional data collection or re-training. Exhaustive experimental results demonstrate that our multi-affordance grasping achieves high success rates for a wide variety of objects in clutter, and our recognition algorithm achieves high accuracy for both known and novel grasped objects. The approach was part of the MIT–Princeton Team system that took first place in the stowing task at the 2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are available online at http://arc.cs.princeton.edu/
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1177/0278364919868017
