ProAct：一种双系统框架用于主动具身社交代理
ProAct: Harnessing Streaming Motion Generation and Agentic Reasoning for Real-Time Embodied Social Interaction
AI总结：
ProAct通过双系统框架实现主动具身社交代理，结合低延迟行为系统与慢速认知系统，提升交互的主动性和社会参与度。
摘要：
具身社交代理近年来在生成同步语音和手势方面取得了进展。然而，大多数交互系统仍然本质上是反应性的，只能在短时间内响应当前的感官输入。主动社交行为则需要对积累的上下文进行推断和意图推断，这与实时交互的严格延迟预算相冲突。我们提出了ProAct，一种双系统框架，通过将低延迟的“行为系统”用于流式多模态交互与较慢的“认知系统”进行分离，该系统执行长时间尺度的社会推理并生成高层次的主动意图。为将推断意图转化为连续的非语言行为而不破坏流畅性，我们引入了一个基于意图的流式流匹配模型，通过ControlNet进行条件化。这种机制支持异步意图注入，使在单个运动流中无缝切换反应性和主动性手势成为可能。我们将在物理人形机器人上部署ProAct，并评估运动质量和交互有效性。在现实世界交互用户研究中，参与者和观察者一致认为ProAct比反应性变体在感知主动性、社会存在感和整体参与度上更优，证明了双系统主动控制在具身社交交互中的优势。
英文摘要：
Real-time embodied social interaction places two equally demanding requirements on an agent: continuously generating fluent multimodal interaction behavior, and proactively reasoning over accumulated dialogue and visual context to decide when to take initiative. These requirements must both be satisfied under a strict latency budget, making them difficult to meet simultaneously. We present ProAct, a dual-system framework that manages these time-critical requirements by integrating a low-latency Behavioral System for streaming multimodal interaction with a slower Cognitive System that performs long-horizon social reasoning and produces high-level proactive intentions. The Cognitive System incorporates an efficient memory mechanism and a user-motivation prediction module to reason over accumulated dialogue and visual context and determine when proactive intervention is appropriate. The Behavioral System further includes an intention-conditioned streaming flow-matching motion generator with a disentangled ControlNet branch, which translates deliberative intentions into continuous non-verbal behavior without disrupting interaction fluency. We deploy ProAct on a physical humanoid robot and validate the framework through comprehensive experiments, including real-world user studies, motion-generation benchmarks, and evaluation on ProActBench, a new, targeted benchmark for evaluating proactive trigger detection and restraint in embodied interaction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
