Emergent Reality: Knowledge Formation from Multimodal Learning through Human-Robot Interaction in Extended Reality

为了减轻用户在交互式、多通道和现场向服务机器人传授新知识时的负担，开发了一种使用混合现实(MR)的人-机器人接口。使用系统可用性量表(SUS)和NASA任务负载指数(NASA-TLX)在三个实验场景中评估了该界面的有效性：1)不与用户共享推理结果，2)通过语音对话共享推理结果(基线)，3)使用MR界面共享推理结果(建议)。与与机器人进行语音对话相比，MR界面显著减少了时间、身体和精神上的负担。研究结果在IEEE/RSJ IROS 2022上公布，并发表在《日刊综合新闻》上。

A human-robot interface using mixed reality (MR) was developed to reduce the user's burden during the interactive, multimodal, and on-site teaching of new knowledge to service robots. The effectiveness of the interface was evaluated using the System Usability Scale (SUS) and NASA Task Load Index (NASA-TLX) in three experimental scenarios: 1) no sharing of inference results with the user, 2) sharing inference results through voice dialogue (baseline), and 3) sharing inference results using the MR interface (proposed). The MR interface significantly reduced temporal, physical, and mental burdens compared to voice dialogue with the robot. The results were presented at IEEE/RSJ IROS 2022 and published in the Nikkan Kogyo Shimbun newspaper.