A Modern System Recipe for Situated Embodied Human-Robot Conversation with Real-Time Multimodal LLMs and Tool-Calling

2026-02-04

AI Summary

This work addresses the challenge of coordinating active perception—such as gaze selection—with language generation in embodied robots under stringent low-latency constraints. To this end, the authors propose a lightweight system framework that tightly integrates a real-time multimodal large language model with attention control mechanisms and interfaces to active perception tools, enabling dynamic perception and context-aware dialogue in a synergistic manner. The approach is validated across six domestic scenarios, demonstrating high accuracy in tool-based decision-making and strong user satisfaction in subjective evaluations. Results show a significant improvement in interaction naturalness and fluency, highlighting the framework’s potential for enabling real-time, high-quality human-robot conversation in complex environments.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Computer Vision: Language and Vision

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Situated embodied conversation requires robots to interleave real-time dialogue with active perception: deciding what to look at, when to look, and what to say under tight latency constraints. We present a simple, minimal system recipe that pairs a real-time multimodal language model with a small set of tool interfaces for attention and active perception. We study six home-style scenarios that require frequent attention shifts and increasing perceptual scope. Across four system variants, we evaluate turn-level tool-decision correctness against human annotations and collect subjective ratings of interaction quality. Results indicate that real-time multimodal large language models and tool use for active perception is a promising direction for practical situated embodied conversation.

Problem

• situated embodied conversation
• real-time multimodal LLMs
• active perception
• tool-calling
• human-robot interaction

Innovation

• real-time multimodal LLMs
• active perception
• tool-calling
• situated embodied conversation
• attention control