Schmidhuber, Jürgen Förster, Alexander Leitner, Jürgen 2014-09-29 Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions. application/pdf https://susi.usi.ch/global/documents/318495 https://localhost:5000/ark:/12658/srd1318495 https://susi.usi.ch/documents/318495/files/2014INFO020.pdf eng info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:rero-006-114691 info:eu-repo/semantics/altIdentifier/ark/12658/srd1318495 info:eu-repo/semantics/openAccess License undefined Robotics Humanoids Machine learning Artificial intelligence Genetic programming Eye-hand coordination Robot learning Computer vision Robotic vision Reactive reaching Visual feedback info:eu-repo/classification/udc/004 Towards adaptive and autonomous humanoid robots : from vision to actions http://purl.org/coar/resource_type/c_db06