Adaptive signal processing
Digital signal processing (3rd ed.): principles, algorithms, and applications
Digital signal processing (3rd ed.): principles, algorithms, and applications
Forward models for physiological motor control
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Active Sensing Capabilities of the Rat Whisker System
Autonomous Robots
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Biomimetic whiskers for shape recognition
Robotics and Autonomous Systems
Whiskerbot: A Robotic Active Touch System Modeled on the Rat Whisker Sensory System
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Using probabilistic reasoning over time to self-recognize
Robotics and Autonomous Systems
Efference copies in neural control of dynamic biped walking
Robotics and Autonomous Systems
The discrete Laguerre transform: derivation and applications
IEEE Transactions on Signal Processing
IEEE Transactions on Robotics
SCRATCHbot: active tactile sensing in a whiskered mobile robot
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Bioinspired adaptive control for artificial muscles
Living Machines'13 Proceedings of the Second international conference on Biomimetic and Biohybrid Systems
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Sensory signals are often caused by one's own active movements. This raises a problem of discriminating between self-generated sensory signals and signals generated by the external world. Such discrimination is of general importance for robotic systems, where operational robustness is dependent on the correct interpretation of sensory signals. Here, we investigate this problem in the context of a whiskered robot. The whisker sensory signal comprises two components: one due to contact with an object (externally generated) and another due to active movement of the whisker (self-generated). We propose a solution to this discrimination problem based on adaptive noise cancelation, where the robot learns to predict the sensory consequences of its own movements using an adaptive filter. The filter inputs (copy of motor commands) are transformed by Laguerre functions instead of the of tenused tapped-delay line, which reduces model order and, therefore, computational complexity. Results from a contact-detection task demonstrate that false positives are significantly reduced using the proposed scheme.