变速示教回放作为模仿学习的真实世界数据增强方法
Variable-Speed Teaching-Playback as Real-World Data Augmentation for Imitation Learning
AI总结：
本文提出变速示教回放作为适用于力控制的真实世界数据增强方法，在仅两次固定速度演示下使模仿学习成功率最高提升55%，并改善指令精度。
摘要：
由于模仿学习依赖难以仿真环境中的真人演示，在该方法中引入力控制后，即便只是简单地改变速度，也会导致训练数据短缺。尽管数据增强领域已针对数据不足问题提出应对方案，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位置控制的下采样。本文提出一种适用于力控制且保留真实世界数据集优势的新型数据增强方法。我们将变速示教回放用作真实世界数据增强，以增加变速条件下环境反应的数量并提升其质量。实验在基于双边控制的模仿学习上进行，采用配备位置-力控制的模仿学习方法。我们在变速条件下对抓取-放置和擦拭两个任务评估了真实世界数据增强的效果，每个任务仅使用来自固定速度的两次真人演示。结果表明，仅通过改变真实世界反应的速度，成功率最高提升了55%，并且通过收集变速条件下的环境反应，沿持续时间/频率指令的精度也得到了改善。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
