REACT: Two Datasets for Analyzing Both Human Reactions and Evaluative Feedback to Robots Over Time
CoRR(2024)
摘要
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.
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