Adaptive Cancelation 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 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 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.

I. Introduction

Active exploration of the environment is a necessary behavioral feature of both animals and mobile robots, for the purposes of navigation, object localization, and object recognition (see, e.g., [1]). However, active movements will often generate sensations in their own right, leading to a discrimination problem: What sensory signals are caused by one's own movements, and what sensory signals are caused by the external world? It is essential that an autonomous agent, either animal or robot, is able to make this distinction in order to interact with the environment in a robust manner. Falsely interpreting sensations could lead to catastrophic consequences for a robot, especially when dealing with threats or opportunities.