Abstract
We present a unified visuomotor neural architecture for the robotic task of identifying, localizing, and grasping a goal object in a cluttered scene. The RetinaNet-based neural architecture enables end-to-end training of visuomotor abilities in a biological-inspired developmental approach. We demonstrate a successful development and evaluation of the method on a humanoid robot platform. The proposed architecture outperforms previous work on single object grasping as well as a modular architecture for object picking. An analysis of grasp errors suggests similarities to infant grasp learning: While the end-to-end architecture successfully learns grasp configurations, sometimes object confusions occur: when multiple objects are presented, salient objects are picked instead of the intended object.
Original language | English |
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Title of host publication | Proceedings of the Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob2019) |
Number of pages | 7 |
Place of Publication | Oslo, Norway |
Publication date | 1 Aug 2019 |
Pages | 19-25 |
DOIs | |
Publication status | Published - 1 Aug 2019 |
Externally published | Yes |