Abstract
This paper presents an effective neural network, Residual Generative Grasping Convolutional Neural Network (ReGG-CNN), to predict reliable robotic grasps for novel objects based on an n-channel scene image. Based on Generative Grasping Convolutional Neural Network (GG-CNN) architecture, ReGG-CNN incorporates residual blocks to enhance performance while preserving the network’s lightweight and single-pass generative nature. ReGG-CNN is evaluated on two standard open-source datasets, Cornell and Jacquard grasping datasets, achieving 87% and 89% accuracy, respectively. Moreover, multiple cluttered scenes are created using novel household and adversarial objects to assess the model’s capability to generalize to most kinds of objects and make multi-grasp predictions. Even though only training on individual objects, ReGG-CNN effectively predicts grasps for diverse objects in cluttered scenes.
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Zhang, Z. et al. (2024). Enhancing Pixel-Wise Robotic Grasping with Residual Blocks. In: Park, J.S., Yang, L.T., Pan, Y., Park, J.J. (eds) Advances in Computer Science and Ubiquitous Computing. CUTECSA 2023. Lecture Notes in Electrical Engineering, vol 1190. Springer, Singapore. https://doi.org/10.1007/978-981-97-2447-5_35
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DOI: https://doi.org/10.1007/978-981-97-2447-5_35
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