Title: An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction,
Abstract: During visuomotor tasks, robots must compensate for temporal delays inherent
in their sensorimotor processing systems. Delay compensation becomes crucial in
a dynamic environment where the visual input is constantly changing, e.g.,
during the interacting with a human demonstrator. For this purpose, the robot
must be equipped with a prediction mechanism for using the acquired perceptual
experience to estimate possible future motor commands. In this paper, we
present a novel neural network architecture that learns prototypical visuomotor
representations and provides reliable predictions on the basis of the visual
input. These predictions are used to compensate for the delayed motor behavior
in an online manner. We investigate the performance of our method with a set of
experiments comprising a humanoid robot that has to learn and generate visually
perceived arm motion trajectories. We evaluate the accuracy in terms of mean
prediction error and analyze the response of the network to novel movement
demonstrations. Additionally, we report experiments with incomplete data
sequences, showing the robustness of the proposed architecture in the case of a
noisy and faulty visual sensor.