Effectiveness of a Coupled Oscillator Network for Surface Discernment by a Quadruped Robot based on Kinesthetic Experience

Inspired by examples of oscillatory circuits in biological brains, we explore a hypothesis that one role of dynamical neural networks observed in biological sensory systems is to amplify subtle differences in sensory data, which in turn simplifies the task of classifying external stimuli. The authors recently developed a method for classifying the surface walked upon by a quadruped robotic dog [Holmstrom, L., et al., 2007]. The method developed utilizes time series data from the dog's joint sensors (kinesthetic vector). Employing the same data, an experiment was set up to explore the above hypothesis, comparing the relative accuracy of classifying (discerning) the surface type experienced by the robot, both with and without the inclusion of a system of coupled nonlinear oscillators in the data processing stream. These experiments demonstrated a significant increase in classification rate (on average) when the sensory data was passed through a coupled oscillator system to precondition the signals prior to inputting to a PNN type neural network classifier, in comparison with the result obtained by feeding the data to the PNN without preconditioning. From an implementation point of view, it is significant that these results were obtained via a coupled oscillator whose inter-oscillator weights were randomly instantiated. Some of the results are provided in terms of the Lyapunov exponent and the spectral radius of the inter-oscillator weight matrix.