Publikation
A Study on the Influence of Task Dependent Anthropomorphic Grasping Poses for Everyday Objects
Niko Kleer; Martin Feick
In: 2022 IEEE-RAS 21st International Conference on Humanoid Robots. IEEE-RAS International Conference on Humanoid Robots (Humanoids-2022), November 28-30, Ginowan, Okinawa, Japan, Pages 829-836, IEEE, 2022.
Zusammenfassung
Robots using anthropomorphic hands and prosthesis 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.