发表机构
ShanghaiTech University; InstAdapt（上海科技大学; InstAdapt）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出ARISE框架，在Sophia人形机器人上统一社会推理与具身交互，通过多模态理解、记忆和流式执行实现自然协调的社交行为，显著提升交互质量并降低延迟。
AI 中文摘要
自然的人与人面对面的人机交互要求机器人理解不断变化的社会情境，决定何时参与互动，并通过协调的物理行为表达其意图。然而，现有方法很少能闭环完成这一过程：基础模型智能体提供了日益强大的多模态推理和记忆能力，但基本上仍是脱离具身的；而富有表现力的虚拟智能体则不需要面对真实机器人的物理约束；物理社交机器人通常仅部分地处理社会推理和具身表达。我们提出了ARISE，一个在Sophia人形机器人上统一智能体推理与交互式社会具身的框架。ARISE整合了多模态情境理解、长期记忆以及反应性和主动性的交互，以决定何时沟通以及沟通什么，并通过流式执行将社会意图转化为与语音和机械面部表情相协调的机器人原生手势。在Sophia上的广泛评估展示了强大的感知交互质量、富有表现力且协调良好的具身行为，以及通过流式执行实现的显著延迟降低。这些结果强调了在与人形机器人进行自然交互时，共同推理沟通内容、参与时机以及如何物理表达社会意图的重要性。项目页面：此https URL
英文摘要
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/
Comments8 pages, 5 figures
