From Social Reasoning to Embodied Interaction: An Agentic Framework for Social Robots

2026-10-05

AI Summary

This study addresses the challenge that existing methods struggle to unify social reasoning with physical expression, resulting in unnatural robotic interactions. We propose ARISE, a framework that deeply integrates agent-level reasoning with embodied interaction to tackle real-time social decision-making under physical constraints. Deployed on the Sophia humanoid robot, ARISE combines multimodal large language models, short- and long-term memory mechanisms, and streaming execution techniques to achieve closed-loop interaction—from comprehending social intent to coordinating mechanical facial expressions and gestures. Experimental results demonstrate that ARISE significantly enhances interaction quality and expressiveness while substantially reducing response latency, thereby validating the critical role of joint reasoning and expression in embodied social intelligence.

Abstract

Natural face-to-face human--robot interaction requires a robot to understand an evolving social situation, decide when to engage, and express its intent through coordinated physical behavior. Yet existing approaches rarely close this loop: foundation-model agents provide increasingly capable multimodal reasoning and memory but remain largely disembodied, while expressive virtual agents do not face the physical constraints of real robots, and physical social robots typically address social reasoning and embodied expression only partially. We present ARISE, a unified framework that bridges Agentic Reasoning and Interactive Social Embodiment on the Sophia humanoid robot. ARISE integrates multimodal context understanding, long-term memory, and reactive and proactive interaction to determine when and what to communicate, and translates social intent into robot-native gestures coordinated with speech and mechanical facial expressions through streaming execution. Extensive evaluations on Sophia demonstrate strong perceived interaction quality, expressive and well-coordinated embodied behavior, and substantial latency reductions through streaming execution. These results highlight the importance of jointly reasoning about what to communicate, when to engage, and how to physically express social intent for natural interaction with humanoid robots. Project Page: https://robosocial.github.io/

Problem

• Social Reasoning
• Embodied Interaction
• Human-Robot Interaction
• Humanoid Robots
• Foundation Models

Innovation

• Agentic Framework
• Social Embodiment
• Multimodal Reasoning
• Streaming Execution
• Humanoid Robot