QUB-PHEO: A Visual-Based Dyadic Multi-View Dataset for Intention Inference in Collaborative Assembly
This dataset addresses the need for rich, annotated data to enhance machine learning models for human-robot interaction in collaborative assembly tasks, though it is incremental as it builds on existing dataset efforts.
The authors tackled the problem of advancing human-robot interaction research by introducing QUB-PHEO, a visual-based dyadic dataset with multimodal annotations for 70 participants, capturing 36 distinct subtasks in assembly operations to improve intention inference models.
QUB-PHEO introduces a visual-based, dyadic dataset with the potential of advancing human-robot interaction (HRI) research in assembly operations and intention inference. This dataset captures rich multimodal interactions between two participants, one acting as a 'robot surrogate,' across a variety of assembly tasks that are further broken down into 36 distinct subtasks. With rich visual annotations, such as facial landmarks, gaze, hand movements, object localization, and more for 70 participants, QUB-PHEO offers two versions: full video data for 50 participants and visual cues for all 70. Designed to improve machine learning models for HRI, QUB-PHEO enables deeper analysis of subtle interaction cues and intentions, promising contributions to the field. The dataset will be available at https://github.com/exponentialR/QUB-PHEO subject to an End-User License Agreement (EULA).