모방학습 기반 로봇 작업 속도 개선을 위한 선행 시간적 앙상블 기법

Abstract

Imitation learning, which enables robots to learn behaviors from demonstrations by humans, has emerged as a promising solution for generating robot motions in such environments. The imitation learning-based robot motion generation method, however, has the drawback of depending on the demonstrator’s task execution speed. This paper presents a novel temporal ensemble approach applied to imitation learning algorithms, allowing for the execution of future actions. The proposed method leverages existing demonstration data and pre-trained policies, offering advantages of requiring no additional computation and being easy to implement. The algorithm’s performance was validated through real-world experiments involving robotic block color sorting, demonstrating up to 3x increase in task execution speed while maintaining a high success rate compared to the action chunking with transformer method. This study highlights the potential for significantly improving the performance of imitation learning-based policies, which were previously limited by the demonstrator’s speed. It is expected to contribute substantially to future advancements in autonomous object manipulation technologies aimed at enhancing productivity.