Vision processing for robot learning

Abstract

Robot learning ‐ be it unsupervised, supervised or self‐supervised ‐ is one method of dealing with noisy, inconsistent, or contradictory data that has proven useful in mobile robotics. In all but the simplest cases of robot learning, raw sensor data cannot be used directly as input to the learning process. Instead, some “meaningful” preprocessing has to be applied to the raw data, before the learning controller can use the sensory perceptions as input. In this paper, two instances of supervised and unsupervised robot learning experiments, using vision input are presented. The vision sensor signal preprocessing necessary to achieve successful learning is also discussed.