触覚フィードバックによる深層模倣学習のデータ品質と量の改善
Leveraging Haptic Feedback to Improve Data Quality and Quantity for Deep Imitation Learning Models
遠隔操作による実演データ収集に触覚フィードバックを追加し、データ収集効率と模倣学習ポリシーの性能が向上することをドア開けタスクで示した。
著者: Catie Cuan, Allison Okamura, Mohi Khansari
分類: cs.RO
原文アブストラクト
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.
