ProAct: A Dual-System Framework for Proactive Embodied Social Agents
- URL: http://arxiv.org/abs/2602.14048v1
- Date: Sun, 15 Feb 2026 08:27:34 GMT
- Title: ProAct: A Dual-System Framework for Proactive Embodied Social Agents
- Abstract summary: Embodied social agents have recently advanced in generating synchronized speech and gestures.<n> Proactive social behavior requires deliberation over accumulated context and intent inference.<n>We present emphProAct, a dual-system framework for streaming multimodal interaction.
- Score: 6.33976966063203
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Embodied social agents have recently advanced in generating synchronized speech and gestures. However, most interactive systems remain fundamentally reactive, responding only to current sensory inputs within a short temporal window. Proactive social behavior, in contrast, requires deliberation over accumulated context and intent inference, which conflicts with the strict latency budget of real-time interaction. We present \emph{ProAct}, a dual-system framework that reconciles this time-scale conflict by decoupling a low-latency \emph{Behavioral System} for streaming multimodal interaction from a slower \emph{Cognitive System} which performs long-horizon social reasoning and produces high-level proactive intentions. To translate deliberative intentions into continuous non-verbal behaviors without disrupting fluency, we introduce a streaming flow-matching model conditioned on intentions via ControlNet. This mechanism supports asynchronous intention injection, enabling seamless transitions between reactive and proactive gestures within a single motion stream. We deploy ProAct on a physical humanoid robot and evaluate both motion quality and interactive effectiveness. In real-world interaction user studies, participants and observers consistently prefer ProAct over reactive variants in perceived proactivity, social presence, and overall engagement, demonstrating the benefits of dual-system proactive control for embodied social interaction.
Related papers
- SocioGesture: Real-Time and Adaptive Social Gesture Perception for Human-Robot Interaction [4.0450087039946006]
We present SocioGesture, a real-time adaptive social gesture perception system for human-robot interaction (HRI)<n>SocioGesture uses a compact confidence-aware body-hand skeleton representation and a lightweight dual-stream model that fuses body motion with hand articulation for low-latency onboard recognition.<n>On a social gesture dataset collected in mixed indoor-outdoor HRI scenarios, SocioGesture achieves strong held-out-subject recognition.<n>During deployment, uncertain interaction segments are saved for offline labeling and adaptation, enabling SocioGesture to expand its gesture vocabulary.
arXiv Detail & Related papers (2026-09-03T23:03:43Z) - F2F-AP: Flow-to-Future Asynchronous Policy for Real-time Dynamic Manipulation [62.06267255986041]
Asynchronous inference has emerged as a prevalent paradigm in robotic manipulation.<n>In this paper, we propose a novel framework that leverages predicted object flow to synthesize future observations.<n>Our approach significantly enhances responsiveness and success rates in complex dynamic manipulation tasks.
arXiv Detail & Related papers (2026-04-02T17:57:15Z) - Real-Time Synchronized Interaction Framework for Emotion-Aware Humanoid Robots [0.5156484100374058]
We present a real-time framework for NAO robots that synchronizes speech prosody with full-body gestures.<n>By enabling seamless sensorimotor coordination, this framework advances the deployment of context-aware social robots.
arXiv Detail & Related papers (2026-01-24T03:53:09Z) - Interact2Ar: Full-Body Human-Human Interaction Generation via Autoregressive Diffusion Models [80.28579390566298]
We introduce Interact2Ar, a text-conditioned autoregressive diffusion model for generating full-body, human-human interactions.<n>Hand kinematics are incorporated through dedicated parallel branches, enabling high-fidelity full-body generation.<n>Our model enables a series of downstream applications, including temporal motion composition, real-time adaptation to disturbances, and extension beyond dyadic to multi-person scenarios.
arXiv Detail & Related papers (2025-12-22T18:59:50Z) - Social Agent: Mastering Dyadic Nonverbal Behavior Generation via Conversational LLM Agents [13.902411927285328]
Social Agent is a novel framework for synthesizing realistic and contextually appropriate co-speech nonverbal behaviors in dyadic conversations.<n>We develop an agentic system driven by a Large Language Model (LLM) to direct the conversation flow and determine appropriate interactive behaviors for both participants.<n>We propose a novel dual-person gesture generation model based on an auto-regressive diffusion model, which synthesizes coordinated motions from speech signals.
arXiv Detail & Related papers (2025-10-06T09:41:37Z) - Chronological Thinking in Full-Duplex Spoken Dialogue Language Models [66.84843878538207]
Chronological Thinking aims to improve response quality in full SDLMs.<n>No additional latency: once the user stops speaking, the agent halts thinking and begins speaking without further delay.<n>Results: Experiments demonstrate the effectiveness of chronological thinking through both objective metrics and human evaluations.
arXiv Detail & Related papers (2025-10-02T10:28:11Z) - MoReact: Generating Reactive Motion from Textual Descriptions [57.642436102978245]
MoReact is a diffusion-based method designed to disentangle the generation of global trajectories and local motions sequentially.<n>Our experiments, utilizing data adapted from a two-person motion dataset, demonstrate the efficacy of our approach.
arXiv Detail & Related papers (2025-09-28T14:31:41Z) - Ready-to-React: Online Reaction Policy for Two-Character Interaction Generation [82.73098356401725]
We propose an online reaction policy, called Ready-to-React, to generate the next character pose based on past observed motions.<n>Each character has its own reaction policy as its "brain", enabling them to interact like real humans in a streaming manner.<n>Our approach can be controlled by sparse signals, making it well-suited for VR and other online interactive environments.
arXiv Detail & Related papers (2025-02-27T18:40:30Z) - Diffusion-Based Imitation Learning for Social Pose Generation [0.0]
Intelligent agents, such as robots and virtual agents, must understand the dynamics of complex social interactions to interact with humans.<n>We explore how using a single modality, the pose behavior, of multiple individuals in a social interaction can be used to generate nonverbal social cues for the facilitator of that interaction.
arXiv Detail & Related papers (2025-01-18T20:31:55Z) - Persistent-Transient Duality: A Multi-mechanism Approach for Modeling
Human-Object Interaction [58.67761673662716]
Humans are highly adaptable, swiftly switching between different modes to handle different tasks, situations and contexts.
In Human-object interaction (HOI) activities, these modes can be attributed to two mechanisms: (1) the large-scale consistent plan for the whole activity and (2) the small-scale children interactive actions that start and end along the timeline.
This work proposes to model two concurrent mechanisms that jointly control human motion.
arXiv Detail & Related papers (2023-07-24T12:21:33Z) - Interaction Transformer for Human Reaction Generation [61.22481606720487]
We propose a novel interaction Transformer (InterFormer) consisting of a Transformer network with both temporal and spatial attentions.
Our method is general and can be used to generate more complex and long-term interactions.
arXiv Detail & Related papers (2022-07-04T19:30:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.