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.

翻译：模仿学习使机器人能够通过人类演示学习行为，已成为在此类环境中生成机器人动作的一种有前景的解决方案。然而，基于模仿学习的机器人动作生成方法存在依赖于演示者任务执行速度的缺陷。本文提出了一种应用于模仿学习算法的新型时序集成方法，该方法允许执行未来动作。所提出的方法利用现有的演示数据和预训练策略，具有无需额外计算且易于实现的优势。通过涉及机器人积木颜色分类的真实世界实验验证了算法性能，与基于Transformer的动作分块方法相比，在保持高成功率的同时，任务执行速度最高提升了3倍。本研究突显了显著改进基于模仿学习策略性能的潜力，这些策略先前受限于演示者的速度。预计该方法将为未来旨在提升生产力的自主物体操作技术的进步作出重要贡献。