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Grasp Configuration Synthesis from 3D Point Clouds with Attention Mechanism

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Abstract

Grasp generation is a crucial task in robotics, especially in unstructured environments, where robots must identify suitable grasp locations on objects and determine the grasp configuration. Recent advances in deep learning have led to the development of end-to-end models for 6-DOF grasp generation that can learn to directly map from input point clouds to grasp configurations without intermediate processing steps. However, these models often treat all points in a scene equally, leading to suboptimal results in cluttered contexts where meaningfulness distributions are disparate due to occlusion. While attention mechanisms have shown promise in improving the accuracy and efficiency of various tasks in occluded scenes, their effectiveness in improving grasp generation performance is still an active area of research. Inspired by this potential, we explore the power of attention mechanisms in improving grasp generation from 3D point clouds. Building upon the previous work with VoteGrasp 2022, we integrate a wide range of attention modules and compare their effects and characteristics to identify the most successful combination for enhancing grasp generation performance. We also extend VoteGrasp by adding a semantic object classification loss to the loss function, making our method more flexible than existing approaches. Based on the detailed experiments and analysis, our research provides valuable insights into the use of attention mechanisms for 3D point cloud grasp generation, highlighting their potential to improve the accuracy and efficiency of robotic systems.

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Data Availability

The DexYCB and FPHAB datasets are publicly available at https://dex-ycb.github.io/ and https://guiggh.github.io/publications/first-person-hands/ respectively.

Code Availibility

Not applicable.

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Authors and Affiliations

  1. FPT University, Hanoi, 10000, Vietnam

    Dinh-Cuong Hoang, Anh-Nhat Nguyen, Van-Duc Vu, Duy-Quang Vu, Van-Thiep Nguyen, Thu-Uyen Nguyen, Cong-Trinh Tran, Khanh-Toan Phan & Ngoc-Trung Ho

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  1. Dinh-Cuong Hoang
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  2. Anh-Nhat Nguyen
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  3. Van-Duc Vu
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All the authors conceived the research, designed and implemented the algorithm, and drafted the submitted version of the paper.

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Correspondence to Dinh-Cuong Hoang.

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Hoang, DC., Nguyen, AN., Vu, VD. et al. Grasp Configuration Synthesis from 3D Point Clouds with Attention Mechanism. J Intell Robot Syst 109, 71 (2023). https://doi.org/10.1007/s10846-023-02007-w

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