Kerzel, MatthiasMatthiasKerzelEppe, ManfredManfredEppeHeinrich, StefanStefanHeinrichAbawi, FaresFaresAbawiWermter, StefanStefanWermter2022-04-252022-04-252019-0819th Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob 2019)http://hdl.handle.net/11420/12354We 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.enbio-inspired visuomotor learningcognitive roboticsDevelopmental roboticsConference Paper10.1109/DEVLRN.2019.8850679Other