Adaptive cancellation of self-generated sensory signals in a whisking robot

Sensory signals are often caused by one’s own active movements. This raises a problem of discriminating between selfgenerated sensory signals and signals generated by the external world. Such discrimination is of general importance for robotic systems, where operational robustness is dependent on 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 cancellation, 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 often-used 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.