Neural architectures for robot intelligence.
We argue that direct experimental approaches to elucidate the architecture of higher brains may benefit from insights gained from exploring the possibilities and limits of artificial control architectures for robot systems. We present some of our recent work that has been motivated by that view and that is centered around the study of various aspects of hand actions since these are intimately linked with many higher cognitive abilities. As examples, we report on the development of a modular system for the recognition of continuous hand postures based on neural nets, the use of vision and tactile sensing for guiding prehensile movements of a multifingered hand, and the recognition and use of hand gestures for robot teaching. Regarding the issue of learning, we propose to view real-world learning from the perspective of data-mining and to focus more strongly on the imitation of observed actions instead of purely reinforcement-based exploration. As a concrete example of such an effort we report on the status of an ongoing project in our laboratory in which a robot equipped with an attention system with a neurally inspired architecture is taught actions by using hand gestures in conjunction with speech commands. We point out some of the lessons learnt from this system, and discuss how systems of this kind can contribute to the study of issues at the junction between natural and artificial cognitive systems.
Ritter
H
H
Neuroinformatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany. helge@techfak.uni-bielefeld.de
Steil
J J
JJ
Nölker
C
C
Röthling
F
F
McGuire
P
P
D000200:Action Potentials; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology; D001931:Brain Mapping; D005868:Gestures; D006225:Hand / Q000502:physiology; D006801:Humans; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000166:cytology / Q000502:physiology*; D010812:Physical Stimulation; D012371:Robotics / Q000379:methods*