发表机构
The University of Hong Kong（香港大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对传统遥操作忽视操作者情感状态的问题，提出情感感知共享自主框架，实时估计情感并选择性辅助，实验显示生产性状态提升39.7%，且不损害用户自主性。
AI 中文摘要
物理遥操作将人类的认知灵活性与机器人的精确性相结合，然而高要求的操作任务常常引发严重的认知负荷、强烈的挫败感和执行中断。传统的共享自主范式主要依赖于基于任务的规则，例如空间误差边界，这些规则忽视了操作者的瞬时情感状态，并可能导致干预控制的不匹配。为了解决这一局限性，我们提出了一种情感感知的共享自主遥操作框架，该框架基于实时操作者状态估计动态调节机器人辅助。该系统从同步的面部视频、心脏信号和双臂运动学中估计操作者的情感状态，输出一个七状态的情感分布和一个三分类的操作抽象（中性、生产性、不利）。当检测到用户处于持续的不利状态时，情感感知辅助会被选择性触发，从而在不造成不必要干扰的情况下保持任务正向参与。实证用户研究（N=30）证实，所提出的情感辅助在不损害用户自主性的情况下，将生产性状态最多提高了39.7%。所收集的数据集是第一个提供连续视觉、生理和操作者双臂运动跟踪的双臂遥操作过程中时间情感状态变化的多模态数据集。我们的多模态融合模型在跟踪时间状态动态方面优于零样本基线（Qwen、MiniCPM-V）。这一实际部署提供了一种新的以人为中心的框架，该框架整合了视觉、生理和运动跟踪，用于物理人机交互。
英文摘要
Physical teleoperation integrates human cognitive flexibility with robotic precision, yet demanding manipulation tasks frequently induce severe cognitive workload, acute frustration, and execution breakdown. Conventional shared autonomy paradigms rely primarily on task-based rules, such as spatial error boundaries, which disregard the operator's transient affective state and risk misaligned control interventions. To address this limitation, we propose an affect-aware shared autonomy teleoperation framework that dynamically modulates robotic assistance based on real-time operator state estimation. The system estimates operator affective states from synchronized facial video, cardiac signals, and bilateral arm kinematics, outputting a seven-state affective distribution and a three-category operational abstraction (neutral, productive, adverse). Affect-aware assistance is selectively triggered when the user is detected in a continuous adverse state, preserving task-positive engagement without unnecessary disruption. The empirical user study ($N = 30$) confirms that the proposed affective assistance increases the productive states by up to 39.7% without compromising user agency. The collected dataset represents the first multimodal dataset that provides continuous visual, physiological, and operator's bilateral motion tracking of temporal affective state shifts during bimanual teleoperation. Our multimodal fusion model outperforms zero-shot baselines (Qwen, MiniCPM-V) in tracking temporal state dynamics. This real-world deployment offers a new human-centric framework that integrates visual, physiological, and motion tracking for physical human-robot interaction.
