梳理机器人自解释技术现状，揭示信任构建的关键挑战
Generating Explanations for Autonomous Robots: a Systematic Review
- 系统回顾机器人解释生成方法，聚焦可解释性设计
- 现有系统仍无法完整覆盖自主机器人的复杂行为
- 适合人机交互、机器人可信性研究者参考
建立人类与机器人之间的信任是机器人学长期关注的问题。在人机交互（HRI）环境中，信任发展的关键在于机器人行为的可理解性。可解释自主机器人（XAR）概念正是为应对这一需求而提出。然而，赋予机器人自我解释能力是一项复杂任务，因其行为涉及多种技能和多样化的子系统。这促使研究者探索广泛的方法来生成关于机器人行为的解释。本文通过系统文献综述，分析了现有机器人解释生成策略，并研究了当前XAR的发展趋势。结果表明，可解释性系统已取得积极进展，但仍无法全面覆盖自主机器人的复杂行为。此外，研究还发现解释性理论概念尚无共识，且亟需建立稳健的方法论以评估解释性方法与工具。
原文摘要 · Abstract (English)
Building trust between humans and robots has long interested the robotics community. Various studies have aimed to clarify the factors that influence the development of user trust. In Human-Robot Interaction (HRI) environments, a critical aspect of trust development is the robot's ability to make its behavior understandable. The concept of an eXplainable Autonomous Robot (XAR) addresses this requirement. However, giving a robot self-explanatory abilities is a complex task. Robot behavior includes multiple skills and diverse subsystems. This complexity led to research into a wide range of methods for generating explanations about robot behavior. This paper presents a systematic literature review that analyzes existing strategies for generating explanations in robots and studies the current XAR trends. Results indicate promising advancements in explainability systems. However, these systems are still unable to fully cover the complex behavior of autonomous robots. Furthermore, we also identify a lack of consensus on the theoretical concept of explainability, and the need for a robust methodology to assess explainability methods and tools has been identified.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。