通过机械臂动作推断操作者情绪，准确率达83.3%。
Inferring Operator Emotions from a Motion-Controlled Robotic Arm
- 利用机械臂运动轨迹分析操作者情绪状态。
- 基于手部动作实现83.3%的情绪识别准确率。
- 无需额外设备，适合远程机器人操作场景。
远程机器人操作者的主观状态会显著影响机器人的运动表现，甚至在用户遵循规程的情况下也会导致意外后果。然而，当前在远程控制场景中对操作者情绪状态的识别仍研究不足。现有方法依赖生理信号或肢体语言，但需额外设备和用户配合，限制了远程操作的灵活性。本文证明，即使未专门设计用于情感表达的远程操控机械臂，其功能性动作仍可通过机器学习系统推断操作者的情绪状态。实验结果显示，基于手部运动所提取的特征可实现83.3%的情绪识别准确率。该系统对当前及未来的远程机器人操作与情感机器人应用具有重要启示。
原文摘要 · Abstract (English)
A remote robot operator's affective state can significantly impact the resulting robot's motions leading to unexpected consequences, even when the user follows protocol and performs permitted tasks. The recognition of a user operator's affective states in remote robot control scenarios is, however, underexplored. Current emotion recognition methods rely on reading the user's vital signs or body language, but the devices and user participation these measures require would add limitations to remote robot control. We demonstrate that the functional movements of a remote-controlled robotic avatar, which was not designed for emotional expression, can be used to infer the emotional state of the human operator via a machine-learning system. Specifically, our system achieved 83.3$\%$ accuracy in recognizing the user's emotional state expressed by robot movements, as a result of their hand motions. We discuss the implications of this system on prominent current and future remote robot operation and affective robotic contexts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。