Real-time collaboration with humans poses challenges due to the different
behavior patterns of humans resulting from diverse physical constraints.
Existing works typically focus on learning safety constraints for
collaboration, or how to divide and distribute the subtasks between the
participating agents to carry out the main task. In contrast, we propose to
learn a human constraints model that, in addition, considers the diverse
behaviors of different human operators. We consider a type of collaboration in
a shared-autonomy fashion, where both a human operator and an assistive robot
act simultaneously in the same task space that affects each other's actions.
The task of the assistive agent is to augment the skill of humans to perform a
shared task by supporting humans as much as possible, both in terms of reducing
the workload and minimizing the discomfort for the human operator. Therefore,
we propose an augmentative assistant agent capable of learning and adapting to
human physical constraints, aligning its actions with the ergonomic preferences
and limitations of the human operator.

通过学习和适应人体约束，实现实时协作的增强助理智能体在共享自主模式下帮助人类操作者尽可能地减轻工作负荷和不适感。

在线学习共享自治中的人类约束

Online Learning of Human Constraints from Feedback in Shared Autonomy

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。