The decisions made by autonomous robots hold substantial influence over how humans perceive their behavior. One way to alleviate potential negative impressions of such decisions by humans and enhance human comprehension of them is through explaining. We introduce visual and textual explanations integrated into robot navigation, considering the surrounding environmental context. To gauge the effectiveness of our approach, we conducted a comprehensive user study, assessing user satisfaction across different forms of explanation representation. Our empirical findings reveal a notable discrepancy in user satisfaction, with significantly higher levels observed for explanations that adopt a multimodal format, as opposed to those relying solely on unimodal representations.

With the rise in the number of robots in our daily lives, human-robot encounters will become more frequent. To improve human-robot interaction (HRI), people will require explanations of robots' actions, especially if they do something unexpected. Our focus is on robot navigation, where we explain why robots make specific navigational choices. Building on methods from the area of Explainable Artificial Intelligence (XAI), we employ a semantic map and techniques from the area of Qualitative Spatial Reasoning (QSR) to enrich visual explanations with knowledge-level spatial information. We outline how a robot can generate visual and textual explanations simultaneously and test our approach in simulation.

With the rise in the number of robots in our daily lives, human-robot encounters will become more frequent. To improve human-robot interaction (HRI), people will require explanations of robots' actions, especially if they do something unexpected. Our focus is on robot navigation, where we explain why robots make specific navigational choices. Building on methods from the area of Explainable Artificial Intelligence (XAI), we employ a semantic map and techniques from the area of Qualitative Spatial Reasoning (QSR) to enrich visual explanations with knowledge-level spatial information. We outline how a robot can generate visual and textual explanations simultaneously and test our approach in simulation.