2022/11/06 by Catie Cuan, Allison M. Okamura, Cuan, Catie +3 · 1 citation
Engineering · #FOS: Computer and information sciences #Robot Manipulation and Learning #Robotics (cs.RO) #Soft Robotics and Applications #Teleoperation and Haptic Systems
paper · pdf · doi:10.48550/arxiv.2211.03020
openalex publication_date 2022/11/06 · openalex created_date 2022/11/13 · openalex updated_date 2026/07/28
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.