Improving robot understanding using conversational AI: demonstration and feasibility study

TL;DR

This paper presents a novel approach to enhance robot understanding through a four-level explanation framework and adaptive conversational AI, demonstrated in collaborative tasks with users to improve human-robot interaction.

Contribution

The study introduces a four-level explanation model and an adaptive dialogue system utilizing conversational AI to improve robot explanations and understanding.

Findings

Feasibility of adaptive dialogue for explanations demonstrated

Improved human-robot interaction in collaborative tasks

Effective transition between explanation levels shown

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.