Optimizing Resolution for Feature Extraction in Robotic Motion Learning

Abstract

This paper presents a feature extraction method for robotic motion learning that optimizes image resolution to the task, thereby minimizing computation time. It utilizes mean-shift algorithms and principal component analysis for feature extraction, reinforcement learning for motion learning, and trial and error for finding the appropriate resolution. When applied to a manipulator pushing an object, the resolution adjustment method reduces the task time from one minute to 21 seconds.