让多机器人系统自动解释为何这样决策，帮人发现并修复错误。
CE-MRS: Contrastive Explanations for Multi-Robot Systems
- 用对比性语言解释系统决策依据，结合任务分配与路径规划数据。
- 用户实验显示，该方法显著提升识别和修正系统错误的能力。
- 适合需要理解复杂多机器人行为的工程师与操作员使用。
随着多机器人系统规模扩大、任务复杂度提升及时间跨度延长，其解决方案往往难以被人类用户完全理解。本文提出一种生成自然语言解释的方法，用于向用户说明系统方案的合理性，或协助用户纠正导致次优解的错误。首先，我们构建了适用于多机器人系统的可泛化对比解释形式化框架；随后，提出一种整合式方法，通过选择性融合多机器人任务分配、调度与运动规划的数据，生成解释。在真实人类操作员的用户研究中，该方法显著提升了用户识别并解决系统错误的能力，从而大幅改善了多机器人团队的整体性能。
原文摘要 · Abstract (English)
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.
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