Learning from demonstration is a proven technique to teach robots new skills. Data quality and quantity play a critical role in the performance of models trained using data collected from human demonstrations. In this paper we enhance an existing teleoperation data collection system with real-time haptic feedback to the human demonstrators; we observe improvements in the collected data throughput and in the performance of autonomous policies using models trained with the data. Our experimental testbed was a mobile manipulator robot that opened doors with latch handles. Evaluation of teleoperated data collection on eight real conference room doors found that adding haptic feedback improved 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. A policy from a imitation learning model trained with data collected while the human demonstrators received haptic feedback performed on average 11% better than its counterpart trained with data collected without haptic feedback, indicating that haptic feedback provided during data collection resulted in improved autonomous policies.

翻译：从示范中学习是教授机器人新技能的一种成熟技术。在利用人类示范收集的数据训练的模型中，数据质量与数量对其性能起着关键作用。本文通过向人类示范者提供实时触觉反馈，增强现有的遥操作数据收集系统；我们观察到，收集数据吞吐量的提升，以及使用这些数据训练的模型所驱动的自主策略性能的改进。实验平台为一台配备门闩把手开门功能的移动操作机器人。在八扇真实会议室门上的遥操作数据收集评估发现，添加触觉反馈使数据吞吐量提高了6%。此外，我们利用收集的数据训练了六个基于图像的深度模仿学习模型，其中三个使用触觉反馈数据，三个未使用。这些模型被用于实现数据收集中所用同类型机器人的自主开门功能。基于人类示范者接收触觉反馈时收集的数据训练的模仿学习模型策略，其平均性能比未使用触觉反馈数据训练的对应模型高出11%，这表明数据收集过程中提供的触觉反馈带来了更优的自主策略。