Interactive and Explainable Robot Learning: A Comprehensive Review
By Esameil Seraj, Georgia Institute of Technology, USA, eseraj3@gatech.edu | Kin Man Lee, Georgia Institute of Technology, USA, klee863@gatech.edu | Zulfiqar Zaidi, Georgia Institute of Technology, USA, zzaidi8@gatech.edu | Qingyu Xiao, Georgia Institute of Technology, USA, qxiao33@gatech.edu | Zhaoxin Li, Georgia Institute of Technology, USA, zli3088@gatech.edu | Arthur Nascimento, Georgia Institute of Technology, USA, anascimento7@gatech.edu | Sanne van Waveren, Georgia Institute of Technology, USA, sanne@gatech.edu | Pradyumna Tambwekar, Georgia Institute of Technology, USA, ptambwekar3@gatech.edu | Rohan Paleja, Georgia Institute of Technology, USA, rpaleja3@gatech.edu | Devleena Das, Georgia Institute of Technology, USA, ddas41@gatech.edu | Matthew Gombolay, Georgia Institute of Technology, USA, matthew.gombolay@cc.gatech.edu
Abstract
This review embarks on a comprehensive exploration of approaches, evaluation methods, and ethical considerations in explainable and interactive systems for robotic applications, distinctly focusing on intelligent systems that are specifically designed for learning automated agents. Given the increasing integration of robots in daily life, it is crucial to focus on intelligent systems that can not only learn and adapt, but can also offer clarity and comprehension for their actions. The interactive component of these systems is thoroughly examined, evaluating the algorithms, the modalities used in interaction, and the significance of mixed-initiative and shared autonomy. We spotlight adaptive and adaptable methods, emphasizing the centrality of user-inspired research and personalized approaches in interactive robotics. A rigorous examination of safety and ethical considerations of these intelligent systems anchors the discussion, including aspects of transparency, privacy, accountability, biases, and psychological well-being. The review evaluates existing metrics and benchmarking standards for such systems and explores their practical applications across domains such as healthcare, domestic tasks, and industrial automation. Concluding with key insights and directions for future research, we provide design guidelines and points of consensus for each subject in order to equip readers with a nuanced understanding of current trends and tools in explainable and interactive robotic systems, paving the way for informed research and application in this dynamic field.
Interactive and Explainable Robot Learning: A Comprehensive Review
This monograph embarks on a comprehensive exploration of approaches, evaluation methods, and ethical considerations in explainable and interactive systems for robotic applications, distinctly focusing on intelligent systems that are specifically designed for learning automated agents.
Given the increasing integration of robots in daily life, it is crucial to focus on intelligent systems that not only can learn and adapt, but also can offer clarity and comprehension for their actions. The interactive component of these systems is thoroughly examined, evaluating the algorithms, the modalities used in interaction, and the significance of mixed-initiative and shared autonomy. Adaptive and adaptable methods, emphasizing the centrality of user-inspired research and personalized approaches in interactive robotics are highlighted. Also included is a rigorous examination of safety and ethical considerations of these intelligent systems, including aspects of transparency, privacy, accountability, biases, and psychological well-being. The book evaluates existing metrics and benchmarking standards for such systems and explores their practical applications across domains such as healthcare, domestic tasks, and industrial automation. The publication concludes with key insights and directions for future research, and design guidelines and points of consensus for each subject are included in order to equip readers with a nuanced understanding of current trends and tools in explainable and interactive robotic systems, paving the way for informed research and application in this dynamic field.
