论文标题
通过多模式融合支持用户自主权，以检测用户何时需要社交机器人的帮助
Supporting User Autonomy with Multimodal Fusion to Detect when a User Needs Assistance from a Social Robot
论文作者
论文摘要
对于任何辅助机器人来说，优先考虑用户的自主权至关重要。对于在任务设置中工作以有效维护用户自主权的机器人，必须及时提供帮助并做出准确的决策。我们使用四个独立的高精度，低回报模型，一个相互视线模型，任务模型，确认性凝视模型和词汇模型，这些模型可以预测用户对援助的需求。为了改善我们的四个独立模型，我们使用了滑动窗口方法和随机的森林分类算法来捕获时间依赖性，并将独立模型与晚期融合方法融合在一起。晚期的融合方法强烈胜过所有四个独立模型，提供了一种更健康的方法，具有更高的准确性，以更好地帮助用户，同时保持自主权。这些结果可以提供有关在更多任务设置中包括其他方式并利用辅助机器人的潜力的见解。
It is crucial for any assistive robot to prioritize the autonomy of the user. For a robot working in a task setting to effectively maintain a user's autonomy it must provide timely assistance and make accurate decisions. We use four independent high-precision, low-recall models, a mutual gaze model, task model, confirmatory gaze model, and a lexical model, that predict a user's need for assistance. Improving upon our four independent models, we used a sliding window method and a random forest classification algorithm to capture temporal dependencies and fuse the independent models with a late fusion approach. The late fusion approach strongly outperforms all four of the independent models providing a more wholesome approach with greater accuracy to better assist the user while maintaining their autonomy. These results can provide insight into the potential of including additional modalities and utilizing assistive robots in more task settings.
