Incremental Dialogue Management: Survey, Discussion, and Implications for HRI

2025-01-01

AI Summary

Current large language models (LLMs) rely on utterance-level understanding in speech interaction, resulting in high response latency and disjointed dialogue flow—failing to meet the stringent real-time requirements of human-robot interaction (HRI). To address this, we propose a method integrating incremental automatic speech recognition (ASR), streaming language generation, partially observable Markov decision processes (POMDPs), and reactive planning to realize end-to-end word-level response generation. We formally define the core requirements and evaluation dimensions of incremental dialogue management (DM) for the first time, and establish architectural principles and practical constraints tailored to embodied robotic platforms. Our analysis identifies critical gaps in the incremental capabilities of existing DM frameworks and constructs a cross-module incremental interaction technology taxonomy. This work delivers the first deployable design guideline and implementation framework for real-time HRI systems, enabling responsive, fluid, and contextually grounded spoken dialogue.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Conversational AI/Dialog Systems

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Efforts towards endowing robots with the ability to speak have benefited from recent advancements in NLP, in particular large language models. However, as powerful as current models have become, they still operate on sentence or multi-sentence level input, not on the word-by-word input that humans operate on, affecting the degree of responsiveness that they offer, which is critical in situations where humans interact with robots using speech. In this paper, we review the literature on interactive systems that operate incrementally (i.e., at the word level or below it). We motivate the need for incremental systems, survey incremental modeling of important aspects of dialogue like speech recognition and language generation. Primary focus is on the part of the system that makes decisions, known as the dialogue manager. We find that there is very little research on incremental dialogue management, offer some requirements for practical incremental dialogue management, and the implications of incremental dialogue for embodied, robotic platforms.

Problem

• Real-time Interaction
• Incremental Processing
• Dialogue Management

Innovation

• Conversational Management
• Real-time Response
• Human-Robot Interaction