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- W4313562945 abstract "Robots using anthropomorphic hands and pros-thesis grasping applications frequently rely on a corpus of labeled images for training a learning model that predicts a suitable grasping pose for grasping an object. However, factors such as an object's physical properties, the intended task, and the environment influence the choice of a suitable grasping pose. As a result, the annotation of such images introduces a level of complexity by itself, therefore making it challenging to establish a systematic labeling approach. This paper presents three crowdsourcing studies that focus on collecting task-dependent grasp pose labels for one hundred everyday objects. Finally, we report on our investigations regarding the influence of task-dependence on the choice of a grasping pose and make our collected data available in the form of a dataset." @default.
- W4313562945 created "2023-01-06" @default.
- W4313562945 creator A5019946319 @default.
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- W4313562945 date "2022-11-28" @default.
- W4313562945 modified "2023-10-11" @default.
- W4313562945 title "A Study on the Influence of Task Dependent Anthropomorphic Grasping Poses for Everyday Objects" @default.
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- W4313562945 doi "https://doi.org/10.1109/humanoids53995.2022.10000198" @default.
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