Because imitation learning relies on human demonstrations in hard-to-simulate settings, the inclusion of force control in this method has resulted in a shortage of training data, even with a simple change in speed. Although the field of data augmentation has addressed the lack of data, conventional methods of data augmentation for robot manipulation are limited to simulation-based methods or downsampling for position control. This paper proposes a novel method of data augmentation that is applicable to force control and preserves the advantages of real-world datasets. We applied teaching-playback at variable speeds as real-world data augmentation to increase both the quantity and quality of environmental reactions at variable speeds. An experiment was conducted on bilateral control-based imitation learning using a method of imitation learning equipped with position-force control. We evaluated the effect of real-world data augmentation on two tasks, pick-and-place and wiping, at variable speeds, each from two human demonstrations at fixed speed. The results showed a maximum 55% increase in success rate from a simple change in speed of real-world reactions and improved accuracy along the duration/frequency command by gathering environmental reactions at variable speeds.

翻译：由于模仿学习依赖于在难以模拟环境中的人类演示，将力控制纳入该方法导致了训练数据的短缺，即使仅进行简单的速度变化。尽管数据增强领域已针对数据不足问题提出了解决方案，但机器人操作的传统数据增强方法仅限于基于仿真的方法或针对位置控制的下采样。本文提出了一种适用于力控制并保留真实世界数据集优势的新型数据增强方法。我们采用变速示教回放作为真实世界数据增强手段，以增加变速环境下环境反应的数量与质量。通过采用配备位置-力控制的模仿学习方法，在基于双边控制的模仿学习上进行了实验。我们评估了真实世界数据增强在两个任务（抓取放置和擦拭）上变速执行的效果，每个任务均源自两个固定速度的人类演示。结果表明，通过简单改变真实世界反应的速度，成功率最高提升55%，并通过收集变速环境反应提高了沿持续时间/频率指令的精度。