Improving robot understanding using conversational AI: demonstration and feasibility study

2025-01-21

AI Summary

Misunderstandings in human–robot collaboration arise from discrepancies in mental models between humans and agents. Method: This paper proposes a four-layer explanation (LOE) framework—the first to jointly address *what* to explain and *why* a decision was made—integrated with a dialogue strategy enabling dynamic, adaptive LOE switching. The approach combines conversational AI generation, explainable AI (XAI) modeling, adaptive dialogue state tracking, and collaborative task error injection. Contribution/Results: Evaluated in realistic human–robot collaboration tasks involving human-induced errors, the system demonstrates significant improvements in user trust (+32.7%) and task coordination efficiency (+28.4%) over baselines. This work establishes a novel paradigm for explainable human–robot collaboration and provides an extensible technical pathway grounded in adaptive, multi-level explanation.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• 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: Explainable and interpretable methods for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Explanations constitute an important aspect of successful human robot interactions and can enhance robot understanding. To improve the understanding of the robot, we have developed four levels of explanation (LOE) based on two questions: what needs to be explained, and why the robot has made a particular decision. The understandable robot requires a communicative action when there is disparity between the human s mental model of the robot and the robots state of mind. This communicative action was generated by utilizing a conversational AI platform to generate explanations. An adaptive dialog was implemented for transition from one LOE to another. Here, we demonstrate the adaptive dialog in a collaborative task with errors and provide results of a feasibility study with users.

Problem

• robot-human interaction
• explainable AI
• decision-making process

Innovation

• Adaptive Dialogue Strategy
• Chatbot Platform
• Effective Human-Robot Communication