Proleptic Temporal Ensemble for Improving the Speed of Robot Tasks Generated by Imitation Learning

TL;DR

This paper introduces a proleptic temporal ensemble method for imitation learning that significantly increases robot task execution speed without extra computation, validated through real-world experiments.

Contribution

It proposes a novel temporal ensemble approach that leverages existing data and pre-trained policies to enhance imitation learning speed.

Findings

Up to 3x increase in task speed

Maintains high success rate

Requires no additional computation

Abstract

Imitation learning, which enables robots to learn behaviors from demonstrations by human, 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 execution of future actions. The proposed method leverages existing demonstration data and pre-trained policies, offering the advantages of requiring no additional computation and being easy to implement. The algorithms 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.