対話駆動型支援ロボティクスにおける曖昧性解消のためのマルチモーダルデータ収集フレームワーク：ウィザード・オブ・オズ予備研究
A Multimodal Data Collection Framework for Dialogue-Driven Assistive Robotics to Clarify Ambiguities: A Wizard-of-Oz Pilot Study
車椅子と搭載ロボットアームの対話制御における曖昧性を解消するため、5種類の同期モダリティを記録するウィザード・オブ・オズ実験フレームワークを提案し、予備データセットで有効性を検証した。
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著者: Guangping Liu, Nicholas Hawkins, Billy Madden, Tipu Sultan, Flavio Esposito, Madi Babaiasl
分類: cs.RO
原文アブストラクト
Integrated control of wheelchairs and wheelchair-mounted robotic arms (WMRAs) has strong potential to increase independence for users with severe motor limitations, yet existing interfaces often lack the flexibility needed for intuitive assistive interaction. Although data-driven AI methods show promise, progress is limited by the lack of multimodal datasets that capture natural Human-Robot Interaction (HRI), particularly conversational ambiguity in dialogue-driven control. To address this gap, we propose a multimodal data collection framework that employs a dialogue-based interaction protocol and a two-room Wizard-of-Oz (WoZ) setup to simulate robot autonomy while eliciting natural user behavior. The framework records five synchronized modalities: RGB-D video, conversational audio, inertial measurement unit (IMU) signals, end-effector Cartesian pose, and whole-body joint states across five assistive tasks. Using this framework, we collected a pilot dataset of 53 trials from five participants and validated its quality through motion smoothness analysis and user feedback. The results show that the framework effectively captures diverse ambiguity types and supports natural dialogue-driven interaction, demonstrating its suitability for scaling to a larger dataset for learning, benchmarking, and evaluation of ambiguity-aware assistive control.
