Leveraging Human Perception in Robot Grasping and Manipulation Through Crowdsourcing and Gamification.

ABSTRACT: Robot grasping in unstructured and dynamic environments is heavily dependent on the object attributes. Although Deep Learning approaches have delivered exceptional performance in robot perception, human perception and reasoning are still superior in processing novel object classes. Furthermore, training such models requires large, difficult to obtain datasets. This work combines crowdsourcing and gamification to leverage human intelligence, enhancing the object recognition and attribute estimation processes of robot grasping. The framework employs an attribute matching system that encodes visual information into an online puzzle game, utilizing the collective intelligence of players to expand the attribute database and react to real-time perception conflicts. The framework is deployed and

SUBMITTER: Gorjup G 

PROVIDER: S-EPMC8116898 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature