Learning from demonstration (LfD) is a proven technique to teach robots new skills. Data quality and quantity play a critical role in LfD trained model performance. In this paper we analyze the effect of enhancing an existing teleoperation data collection system with real-time haptic feedback; we observe improvements in the collected data throughput and its quality for model training. Our experiment testbed was a mobile manipulator robot that opened doors with latch handles. Evaluation of teleoperated data collection on eight real world conference room doors found that adding the haptic feedback improved the data throughput by 6%. We additionally used the collected data to train six image-based deep imitation learning models, three with haptic feedback and three without it. These models were used to implement autonomous door-opening with the same type of robot used during data collection. Our results show that a policy from a behavior cloning model trained with haptic data performed on average 11% better than its counterpart with no haptic feedback data, indicating that haptic feedback resulted in collection of a higher quality dataset.
翻译:从演示中学习(LfD) 已被证明是教给机器人新技能的一种技术。数据质量和数量在LfD培训模型性能中发挥着关键作用。 在本文中,我们分析了用实时机能反馈加强现有的远程操作数据收集系统的效果;我们观察了所收集的数据输送量及其培训质量的改进。我们的实验试验床是一个移动操纵机机器人,用拉链把手打开门。对8个真实世界会议室门上的远程操作数据收集的评估发现,添加简便反馈使数据通过量提高了6%。我们还利用所收集的数据培训了6个基于图像的深层模拟学习模型,3个有机能反馈,3个没有这种模型。这些模型被用来用数据收集中使用的同样类型的机器人进行自动门打开。我们的结果显示,一种行为克隆模型所训练的机能性数据平均比对口好11%,而没有机能反馈数据,这表明不易的反馈导致收集质量更高的数据集。