CE-MRS: Contrastive Explanations for Multi-Robot Systems

TL;DR

This paper presents a formalism and a holistic approach for generating contrastive natural language explanations in multi-robot systems, improving human understanding and error correction capabilities.

Contribution

It introduces a generalizable formalism for contrastive explanations and a comprehensive method for multi-robot scenarios, enhancing interpretability and user assistance.

Findings

User studies show improved error identification and correction.

Enhanced multi-robot team performance with explanations.

Significant user understanding improvements.

Abstract

As the complexity of multi-robot systems grows to incorporate a greater number of robots, more complex tasks, and longer time horizons, the solutions to such problems often become too complex to be fully intelligible to human users. In this work, we introduce an approach for generating natural language explanations that justify the validity of the system's solution to the user, or else aid the user in correcting any errors that led to a suboptimal system solution. Toward this goal, we first contribute a generalizable formalism of contrastive explanations for multi-robot systems, and then introduce a holistic approach to generating contrastive explanations for multi-robot scenarios that selectively incorporates data from multi-robot task allocation, scheduling, and motion-planning to explain system behavior. Through user studies with human operators we demonstrate that our integrated contrastive explanation approach leads to significant improvements in user ability to identify and solve system errors, leading to significant improvements in overall multi-robot team performance.