Structured and Unstructured Speech2Action Frameworks for Human–Robot Collaboration: A User Study

Le résumé fourni par la source

Practical and intuitive communication remains a critical challenge in Human-Robot Collaboration, particularly within domestic environments. Conventional systems typically rely on structured (scripted) speech inputs, which may limit natural interaction and accessibility. This study evaluates user preferences and system usability between structured and unstructured (conversational) speech modalities in a collaborative cooking scenario using a mobile manipulator robot. Thirty adult participants engaged in tasks involving both communication modes, during which the frequency and impact of robot execution errors were also assessed. The proposed Speech2Action framework integrates Google Cloud Speech-to-Text, BERT, and GPT-Neo models for intent recognition and command generation, combined with ROS-based motion control for object retrieval. Usability and perception were analyzed using System Usability Scale (SUS) and Human–Robot Collaboration Questionnaire (HRCQ) metrics through paired t-tests and correlation analyses. Results show a significant preference for unstructured speech (p = 0.0032) with higher SUS scores, while robot execution errors affected perceived safety but not overall usability, consistent with the Pratfall Effect. The findings inform the design of natural, robust, and user-centric speech interfaces for collaborative robots.

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