Abstract
Robots that carry out tasks and interact in complex environments will inevitably commit errors. Error detection is thus an important ability for robots to master, to work in an efficient and productive way. People leverage social cues from others around them to recognize and repair their own mistakes. With advances in computing and AI, it is increasingly possible for robots to achieve a similar error detection capability. In this work, we review current literature around the topic of how social cues can be used to recognize task failures for human-robot interaction (HRI). This literature review unites insights from behavioral science, human-robot interaction, and machine learning, to focus on three areas: 1) social cues for error detection (from behavioral science), 2) recognizing task failures in robots (from HRI), and 3) approaches for autonomous detection of HRI task failures based on social cues (from machine learning). We propose a taxonomy of error detection based on self-awareness and social feedback. Finally, we leave recommendations for HRI researchers and practitioners interested in developing robots that detect (physical) task errors using social cues from bystanders.
Abstract (translated)
在复杂的环境中执行任务并与他人交互的机器人不可避免地会犯错误。因此,错误检测对于机器人来说是非常重要的能力,以以高效、生产率为目标的方式进行工作。人们利用周围的社交暗示来识别和修复自己的错误。随着计算机科学和人工智能的进步,机器人越来越有可能实现类似的错误检测能力。在本文中,我们综述了当前关于如何使用社交暗示来识别人类机器人交互中的任务失败(HRI)的相关文献。这个文献综述融合了行为科学、人类机器人互动和机器学习的见解,重点讨论了三个领域:1)社交暗示用于错误检测(从行为科学的角度);2)机器人识别任务失败的机制(从HRI的角度);3)基于社交暗示的自主检测HRI任务失败的方法和策略(从机器学习的角度)。我们提出了基于自我意识和社交反馈的错误检测分类系统。最后,我们留给HRI研究人员和从业者有关开发使用社交暗示从旁观者处识别(物理)任务错误的机器人的建议。
URL
https://arxiv.org/abs/2301.11972
