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Picking out the Impurities: Attention-based Push-Grasping in Dense Clutter

Published online by Cambridge University Press:  25 March 2022

Ning Lu ,
Yinghao Cai ,
Tao Lu Open the ORCID record for Tao Lu [Opens in a new window] ,
Xiaoge Cao ,
Weiyan Guo  and
Shuo Wang
Ning Lu
Affiliation:
China Waterborne Transport Research Institute, Beijing, 100088, China
Yinghao Cai
Affiliation:
The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
Tao Lu*
Affiliation:
The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
Xiaoge Cao
Affiliation:
The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
Weiyan Guo
Affiliation:
The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
Shuo Wang
Affiliation:
The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
*
*Corresponding author. E-mail: tao.lu@ia.ac.cn
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Abstract

“Picking out the impurities” is a typical scenario in production line which is both time consuming and laborious. In this article, we propose a target-oriented robotic push-grasping system which is able to actively discover and pick the impurities in dense environments with the synergies between pushing and grasping actions. First, we propose an attention module, which includes target saliency detection and density-based occluded-region inference. Without the necessity of expensive labeling of semantic segmentation, our attention module can quickly locate the targets in the view or predict the candidate regions where the targets are most likely to be occluded. Second, we propose a push–grasp synergy framework to sequentially select proper actions in different situations until all targets are picked out. Moreover, we introduce an active pushing mechanism based on a novel metric, namely Target-Centric Dispersion Degree (TCDD) for better grasping. TCDD describes whether the targets are isolated from the surrounding objects. With this metric, the robot becomes more focused on the actions around the targets and push irrelevant objects away. Experimental results on both simulated environment and real-world environment show that our proposed system outperforms several baseline approaches,which also has the capability to be generalized to new scenarios.

Keywords

saliency Detectionrobot Learningpush–grasptarget-oriented Graspingdeep Q Network
Type
Research Article
Information
Robotica , Volume 41 , Issue 2 , February 2023 , pp. 470 - 485
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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