Vision-guided robot manipulator control as learning and recall using SHOSLIF

Abstract

We present a general framework by which a robotic hand-eye system can perform learned tasks by recalling the action sequences that it has learned. In the training phase, the system learns the relationship between the sensors and the actuators from a series of training examples supplied interactively by a system trainer. The system automatically builds a recursive partition tree (RPT) which approximates the mapping from the input to the output. Each node of the RPT represents a cell of the space which is further partitioned by its children via a Voronoi tessellation. Each leaf node corresponds to a training sample and stores the corresponding output. In the performance phase, given an input, the RPT is used to retrieve the desired output by interpolating among all the leaf nodes that are good matches to the input. The RPT results in a logarithmic average time complexity in the number of stored training samples. Such a mechanism is used to accomplish major components of the system, including stereo calibration and sensor-based action sequence learning and execution. A hand-eye system with a PUMA 560 robotic manipulator is used to test the method.