# MGpi: A Computational Model of Multiagent Group Perception and Interaction

## TL;DR

This paper introduces MGpi, a neural network model for socially intelligent multi-agent interactions, featuring a novel KPM gate that filters social cues for better group perception and interaction prediction.

## Contribution

The paper presents MGpi, a deep neural network with a KPM gate for social signal filtering, trained via imitation learning in a social simulator, achieving state-of-the-art group identification performance.

## Key findings

- KPM gate effectively filters social cues from body gestures and spatial behavior.
- MGpi achieves state-of-the-art results in group identification without explicit annotations.
- The model demonstrates improved social interaction prediction in multi-agent environments.

## Abstract

Toward enabling next-generation robots capable of socially intelligent interaction with humans, we present a $\mathbf{computational\; model}$ 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 $\mathbf{social\; interaction\; simulator}$. Further, we demonstrate the efficacy of the KPM gate as a social attention mechanism, achieving state-of-the-art performance on the task of $\mathbf{group\; identification}$ without using explicit group annotations, layout assumptions, or manually chosen parameters.