An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction

TL;DR

This paper introduces an incremental neural network architecture that learns visuomotor representations and predicts future movements to compensate for sensorimotor delays in robots, enhancing real-time interaction capabilities.

Contribution

The novel architecture enables online learning and prediction of visuomotor trajectories, improving robot delay compensation in dynamic environments.

Findings

Achieves accurate predictions with low mean error.

Robust to noisy and incomplete visual data.

Effective in real-time humanoid robot experiments.

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.