In human-robot collaboration, shared control presents an opportunity to teleoperate robotic manipulation to improve the efficiency of manufacturing and assembly processes. Robots are expected to assist in executing the user's intentions. To this end, robust and prompt intention estimation is needed, relying on behavioral observations. The framework presents an intention estimation technique at hierarchical levels i.e., low-level actions and high-level tasks, by incorporating multi-scale hierarchical information in neural networks. Technically, we employ hierarchical dependency loss to boost overall accuracy. Furthermore, we propose a multi-window method that assigns proper hierarchical prediction windows of input data. An analysis of the predictive power with various inputs demonstrates the predominance of the deep hierarchical model in the sense of prediction accuracy and early intention identification. We implement the algorithm on a virtual reality (VR) setup to teleoperate robotic hands in a simulation with various assembly tasks to show the effectiveness of online estimation.

翻译：在人机协作中，共享控制为遥操作机器人操控以提升制造与装配流程效率提供了契机。机器人需要基于行为观测协助执行用户意图。为此，必须实现稳健且即时的意图估计。本文提出一种融合神经网络中多尺度层次化信息的意图估计框架，可在层次化层级（即低级动作与高级任务）运作。在技术层面，我们采用层次依赖损失函数以提升整体准确率。进一步，我们提出一种多窗口方法，为输入数据分配恰当的层次化预测窗口。对不同输入预测能力的分析表明，该深度层次化模型在预测准确率与早期意图识别方面具有显著优势。我们在虚拟现实（VR）实验平台上实现了该算法，通过多种装配任务的仿真遥操作操控验证了在线估计的有效性。