  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.
