让机器人根据用户偏好自动生成解释，提升导航透明度。
Towards Probabilistic Planning of Explanations for Robot Navigation
- 用概率模型建模用户对解释的偏好，动态调整说明策略。
- 能预判不同用户需要的解释类型，提升个性化沟通效果。
- 适合人机交互中需高透明度的机器人系统开发人员。
在机器人领域，确保自主系统对用户可理解、可问责，是实现有效人机交互的关键。本文提出一种将用户中心设计原则融入机器人路径规划核心的新方法。我们构建了一个用于自动化生成机器人导航解释的概率框架，通过概率化建模不同用户对解释的偏好，以适应真实人机交互中的不确定性，并优化机器人决策与行为向人类的传达方式。该方法旨在增强机器人路径规划的透明性，通过预测满足个体用户需求的解释类型，实现对多样化解释需求的动态响应。
原文摘要 · Abstract (English)
In robotics, ensuring that autonomous systems are comprehensible and accountable to users is essential for effective human-robot interaction. This paper introduces a novel approach that integrates user-centered design principles directly into the core of robot path planning processes. We propose a probabilistic framework for automated planning of explanations for robot navigation, where the preferences of different users regarding explanations are probabilistically modeled to tailor the stochasticity of the real-world human-robot interaction and the communication of decisions of the robot and its actions towards humans. This approach aims to enhance the transparency of robot path planning and adapt to diverse user explanation needs by anticipating the types of explanations that will satisfy individual users.
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