Hierarchical Deep Learning for Intention Estimation of Teleoperation Manipulation in Assembly Tasks

arXiv:2403.19770v1 Announce Type: cross
Abstract: 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 …

abstract arxiv assembly collaboration control cs.ai cs.lg cs.ro deep learning efficiency hierarchical human manipulation manufacturing processes prompt robot robotic robotic manipulation robots robust tasks teleoperation type