DA2Dataset: Toward Dexterity-Aware Dual-Arm Grasping

Zhai G, Zheng Y, Xu Z, Kong X, Liu Y, Busam B, Ren Y, Navab N, Zhang Z (2022)

Publication Type: Journal article

Publication year: 2022

Journal

IEEE Robotics and Automation Letters IEEE

Book Volume: 7

Pages Range: 8941-8948

Journal Issue: 4

DOI: 10.1109/LRA.2022.3189959

Abstract

In this paper, we introduce DA^2, the first large-scale dual-arm dexterity-aware dataset for the generation of optimal bimanual grasping pairs for arbitrary large objects. The dataset contains about 9 M pairs of parallel-jaw grasps, generated from more than 6000 objects and each labeled with various grasp dexterity measures. In addition, we propose an end-to-end dual-arm grasp evaluation model trained on the rendered scenes from this dataset. We utilize the evaluation model as our baseline to show the value of this novel and nontrivial dataset by both online analysis and real robot experiments.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Tencent Holdings Ltd / 腾讯控股有限公司 CN China (CN) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) Zhejiang University CN China (CN)

How to cite

APA:

Zhai, G., Zheng, Y., Xu, Z., Kong, X., Liu, Y., Busam, B.,... Zhang, Z. (2022). DA2Dataset: Toward Dexterity-Aware Dual-Arm Grasping. IEEE Robotics and Automation Letters, 7(4), 8941-8948. https://doi.org/10.1109/LRA.2022.3189959

MLA:

Zhai, Guangyao, et al. "DA2Dataset: Toward Dexterity-Aware Dual-Arm Grasping." IEEE Robotics and Automation Letters 7.4 (2022): 8941-8948.

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