preprints.org > engineering > control and systems engineering > doi: 10.20944/preprints202410.0076.v1

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 1 October 2024 / Approved: 1 October 2024 / Online: 2 October 2024 (08:03:50 CEST)

Robot grasp modeling and implementation is one of essential abilities for a robot with embodied artificial intelligence. However, most existing deep learning-based grasp methods suffer a large number of parameters and heavy computation overheads. To address this issue, by fully exploiting the complementary capabilities of both CNNs and Transformers, we propose a smart grasp deep network with one-way fusion strategy via context path and spatial path (SGNet), which enjoys a lightweight structure, fast inference speed and easy deployment on devices with limited computation resources. Specifically, the context path employs lightweight depthwise separable convolution to achieve fast down-sampling while a novel DSFormer module mainly by integrating Transformer is to extract global and context-rich features. The spatial path efficiently fuses feature information from the context path in one-way manner and generate high-resolution feature maps via point-by-point convolution operations. Experimental results show the proposed model with only 1M parameters has a significantly overall performance, achieving 99.4% accuracy on Cornell dataset and 93.4% accuracy on Jacquard dataset, as well as within 12.5ms inference time.

robot grasp; lightweight structure; deep network; fusion strategy; transformer

Engineering, Control and Systems Engineering

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