Automatic Generation of Robot Program Code: Learning from Perceptual Data

Abstract

We propose a novel approach to program a robot by demonstrating the task multiple number of times in front of a vision system. Here we integrate human dexterity with sensory data using computer vision techniques in a single platform. A simultaneous feature detection and tracking framework is used to track various features (finger tips and the wrist joint). A Kalman filter does the tracking by predicting the tentative feature location and a HOS-based data clustering algorithm extracts the feature. Color information of the features are used for establishing correspondences. A fast, efficient and robust algorithm for the vision system thus developed process a binocular video sequence to obtain the trajectories and the orientation information of the end effector. The concept of a trajectory bundle is introduced to avoid singularities and to obtain an optimal path.