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.

研究使用多个预测模型来预测需要协助的用户，并借助滑动窗口和随机森林分类算法的决策融合来提高其准确性，以维护用户的自主权和辅助机器人在更广泛任务设置中的应用潜力。