Authors:
Jens Kleesiek
1
;
Stephanie Badde
2
;
Stefan Wermter
2
and
Andreas K. Engel
3
Affiliations:
1
University Medical Center Hamburg-Eppendorf and University of Hamburg, Germany
;
2
University of Hamburg, Germany
;
3
University Medical Center Hamburg-Eppendorf, Germany
Keyword(s):
Active perception, RNNPB, Humanoid robot.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Cognitive Robotics
;
Cognitive Systems
;
Computational Intelligence
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Informatics in Control, Automation and Robotics
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Robotics and Automation
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
;
Theory and Methods
;
Vision and Perception
Abstract:
We present a recurrent neural architecture with parametric bias for actively perceiving objects. A humanoid
robot learns to extract sensorimotor laws and based on those to classify eight objects by exploring their multi-modal
sensory characteristics. The network is either trained with prototype sequences for all objects or just two
objects. In both cases the network is able to self-organize the parametric bias space into clusters representing
individual objects and due to that, discriminates all eight categories with a very low error rate. We show that
the network is able to retrieve stored sensory sequences with a high accuracy. Furthermore, trained with only
two objects it is still able to generate fairly accurate sensory predictions for unseen objects. In addition, the
approach proves to be very robust against noise.