組立作業における遠隔操作マニピュレーションの意図推定のための階層的深層学習
Hierarchical Deep Learning for Intention Estimation of Teleoperation Manipulation in Assembly Tasks
遠隔操作による組立作業で、ユーザの低レベル動作と高レベルタスクの意図を階層的に推定する深層学習手法を提案し、VR環境で有効性を示した。
著者: Mingyu Cai, Karankumar Patel, Soshi Iba, Songpo Li
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
In human-robot collaboration, shared control presents an opportunity to teleoperate robotic manipulation to improve the efficiency of manufacturing and assembly processes. Robots are expected to assist in executing the user's intentions. To this end, robust and prompt intention estimation is needed, relying on behavioral observations. The framework presents an intention estimation technique at hierarchical levels i.e., low-level actions and high-level tasks, by incorporating multi-scale hierarchical information in neural networks. Technically, we employ hierarchical dependency loss to boost overall accuracy. Furthermore, we propose a multi-window method that assigns proper hierarchical prediction windows of input data. An analysis of the predictive power with various inputs demonstrates the predominance of the deep hierarchical model in the sense of prediction accuracy and early intention identification. We implement the algorithm on a virtual reality (VR) setup to teleoperate robotic hands in a simulation with various assembly tasks to show the effectiveness of online estimation.
