Leveraging Haptic Feedback to Improve Data Quality and Quantity for Deep Imitation Learning Models.

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.

OUTPUT: Choreographed and Reconfigured Human and Industrial Robot Bodies Across Artistic Modalities.

Millions of industrial robots are used across manufacturing and research applications worldwide. Handfuls of these robots have been used in dance, installation, and theatrical art works as tools and performers. OUTPUT, a collaborative artwork presented here, employs an industrial robot as choreographic source material and dancing body in order to reframe these robots as performers and bring them into closer proximity with the general public. This OUTPUT work has existed as a performance, installation, and augmented reality application. All three formats of the work include improvisational components, where a human can dance with a representation of themselves alongside an industrial robot, facilitating an embodied and creative experience next to these sequestered machines.