Papers โบ MGpi: A Computational Model of Multiagent Group Perception and Interaction
MGpi: A Computational Model of Multiagent Group Perception and Interaction
Navyata Sanghvi, Ryo Yonetani, Kris Kitani
Toward enabling next-generation robots capable of socially intelligent interaction with humans, we present a ๐๐จ๐ฆ๐ฉ๐ฎ๐ญ๐๐ญ๐ข๐จ๐ง๐๐ฅ ๐ฆ๐จ๐๐๐ฅ of interactions in a social environment of multiple agents and multiple groups. The Multiagent Group Perception and Interaction (MGpi) network is a deep neural network that predicts the appropriate social action to execute in a group conversation (e.g., speak, listen, respond, leave), taking into account neighbors' observable features (e.g., location of people, gaze orientation, distraction, etc.). A central component of MGpi is the Kinesic-Proxemic-Message (KPM) gate, that performs social signal gating to extract important information from a group conversation. In particular, KPM gate filters incoming social cues from nearby agents by observing their body gestures (kinesics) and spatial behavior (proxemics). The MGpi network and its KPM gate are learned via imitation learning, using demonstrations from our designed ๐ฌ๐จ๐๐ข๐๐ฅ ๐ข๐ง๐ญ๐๐ซ๐๐๐ญ๐ข๐จ๐ง ๐ฌ๐ข๐ฆ๐ฎ๐ฅ๐๐ญ๐จ๐ซ. Further, we demonstrate the efficacy of the KPM gate as a social attention mechanism, achieving state-of-the-art performance on the task of ๐ ๐ซ๐จ๐ฎ๐ฉ ๐ข๐๐๐ง๐ญ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง without using explicit group annotations, layout assumptions, or manually chosen parameters.
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