Conveyor Picking Task with Bilateral Control-Based Imitation Learning and Image Processing

Abstract

Much research for motion generation using machine learning is keeping attention for adaptation to various environments. Imitation learning that can generate robot motions from human demonstrations is a good candidate. We conventionally showed the usefulness of bilateral control to collect motion data for imitation learning. However, image information has not be fully utilized in the method. In particular, the picking task, which has been widely studied as a task involving contact with objects, has not yet been achieved. In this study, we set a conveyor picking task as the target task and verify a learning method that combines ibilateral control based imitation learning and image processing.