Recent work in Human-Robot Interaction (HRI) has shown that robots can leverage implicit communicative signals from users to understand how they are being perceived during interactions. For example, these signals can be gaze patterns, facial expressions, or body motions that reflect internal human states. To facilitate future research in this direction, we contribute the REACT database, a collection of two datasets of human-robot interactions that display users' natural reactions to robots during a collaborative game and a photography scenario. Further, we analyze the datasets to show that interaction history is an important factor that can influence human reactions to robots. As a result, we believe that future models for interpreting implicit feedback in HRI should explicitly account for this history. REACT opens up doors to this possibility in the future.

翻译：在人机交互（HRI）领域的近期研究表明，机器人能够利用来自用户的隐含交流信号来理解其在交互过程中的被感知状态。例如，这些信号可以是反映人类内在状态的目光模式、面部表情或身体动作。为促进该方向的未来研究，我们贡献了REACT数据库——包含两个人类-机器人交互数据集，分别记录用户在协作游戏和摄影场景中对机器人的自然反应。进一步，我们通过数据分析证明交互历史是影响人类对机器人反应的重要因素。因此，我们认为未来用于阐释HRI中隐含反馈的模型应当明确考虑这种历史因素。REACT为这一可能性开启了未来研究之门。