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Open Access

Grasp Detection with Hierarchical Multi-Scale Feature Fusion and Inverted Shuffle Residual

Wenjie Geng1,2,†, Zhiqiang Cao1,2( ), Peiyu Guan1,2,†, Fengshui Jing1,2, Min Tan1,2, Junzhi Yu3
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, China

† Wenjie Geng and Peiyu Guan contribute equally to this work.

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Abstract

Grasp detection plays a critical role for robot manipulation. Mainstream pixel-wise grasp detection networks with encoder-decoder structure receive much attention due to good accuracy and efficiency. However, they usually transmit the high-level feature in the encoder to the decoder, and low-level features are neglected. It is noted that low-level features contain abundant detail information, and how to fully exploit low-level features remains unsolved. Meanwhile, the channel information in high-level feature is also not well mined. Inevitably, the performance of grasp detection is degraded. To solve these problems, we propose a grasp detection network with hierarchical multi-scale feature fusion and inverted shuffle residual. Both low-level and high-level features in the encoder are firstly fused by the designed skip connections with attention module, and the fused information is then propagated to corresponding layers of the decoder for in-depth feature fusion. Such a hierarchical fusion guarantees the quality of grasp prediction. Furthermore, an inverted shuffle residual module is created, where the high-level feature from encoder is split in channel and the resultant split features are processed in their respective branches. By such differentiation processing, more high-dimensional channel information is kept, which enhances the representation ability of the network. Besides, an information enhancement module is added before the encoder to reinforce input information. The proposed method attains 98.9% and 97.8% in image-wise and object-wise accuracy on the Cornell grasping dataset, respectively, and the experimental results verify the effectiveness of the method.

Keywords

grasp detectionhierarchical multi-scale feature fusionskip connections with attentioninverted shuffle residual

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Tsinghua Science and Technology
Volume 29 Issue 1,
February 2024
Pages 244-256
DOI: 10.26599/TST.2023.9010003
Cite this article:
Geng W, Cao Z, Guan P, et al. Grasp Detection with Hierarchical Multi-Scale Feature Fusion and Inverted Shuffle Residual. Tsinghua Science and Technology, 2024, 29(1): 244-256. https://doi.org/10.26599/TST.2023.9010003

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Received: 02 September 2022
Revised: 07 January 2023
Accepted: 15 January 2023
Published: 21 August 2023
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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