让机器人模仿学习动作更快，速度最高提升3倍。
Proleptic Temporal Ensemble for Improving the Speed of Robot Tasks Generated by Imitation Learning
- 用时间预测集成法提前规划未来动作，不增加计算量。
- 实测分拣任务速度最高提升3倍，成功率仍很高。
- 适合想提速机器人动作的工程师或研究者。
模仿学习使机器人能从人类示范中学习行为，但其动作执行速度受限于示范者。本文提出一种新型时间集成方法，应用于模仿学习算法，可提前执行未来动作。该方法利用已有示范数据和预训练策略，无需额外计算，易于实现。通过真实机器人分块颜色排序实验验证，相比基于Transformer的动作分块方法，任务执行速度最高提升3倍，同时保持高成功率。研究证明，该方法能显著提升模仿学习策略的性能，突破原有时速限制，有望推动自主物体操作技术发展，提升生产效率。
原文摘要 · Abstract (English)
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.
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