Multimodal Object Categorization with Reduced User Load through Human-Robot Interaction in Mixed Reality
Multimodal Object Categorization with Reduced User Load through Human-Robot Interaction in Mixed Reality
复制标题
通过混合现实中的人机交互进行多模式对象分类，减少用户负载
DOI：
10.1109/iros47612.2022.9981374
复制
发表时间：
2022
期刊：
影响因子：
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通讯作者：
Taniguchi Tadahiro
中科院分区：
文献类型：
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作者：
Nakamura Hitoshi;Hafi Lotfi El;Taniguchi Akira;Hagiwara Yoshinobu;Taniguchi Tadahiro
使机器人能够从与用户的互动中学习对于执行服务任务至关重要。然而，作为机器人分类对象从多模态信息获得的传感器在交互式现场教学，推断的未知对象的名称并不总是匹配人类用户的期望，特别是当机器人被引入到新的环境。通过与机器人的自然语音交互来演示学习结果通常会给用户带来额外的负担，因为用户只能听机器人来验证结果。因此，我们提出了一个人机界面，以减少用户的负担，通过可视化的推断结果在混合现实（MR）。特别是，我们评估了系统可用性量表（SUS）和NASA任务负荷指数的建议接口（NASA-TLX），具有基于多模态潜在狄利克雷分配（MLDA）的三个实验对象分类场景，其中机器人：1）完全不与用户共享推断结果，2）通过与用户的语音交互来共享推断结果（基线），以及3）通过MR接口（建议的）与用户共享推断的结果。我们表明，通过MR接口提供反馈显着减少了人类用户的时间，身体和精神负担相比，语音与机器人的交互。
Enabling robots to learn from interactions with users is essential to perform service tasks. However, as a robot categorizes objects from multimodal information obtained by its sensors during interactive onsite teaching, the inferred names of unknown objects do not always match the human user's expectation, especially when the robot is introduced to new environments. Confirming the learning results through natural speech interaction with the robot often puts an additional burden on the user who can only listen to the robot to validate the results. Therefore, we propose a human-robot interface to reduce the burden on the user by visualizing the inferred results in mixed reality (MR). In particular, we evaluate the proposed interface on the system usability scale (SUS) and the NASA task load index (NASA-TLX) with three experimental object categorization scenarios based on multimodal latent Dirichlet allocation (MLDA) in which the robot: 1) does not share the inferred results with the user at all, 2) shares the inferred results through speech interaction with the user (baseline), and 3) shares the inferred results with the user through an MR interface (proposed). We show that providing feedback through an MR interface significantly reduces the temporal, physical, and mental burden on the human user compared to speech interaction with the robot.