Self-Explaining Social Robots: An Explainable Behavior Generation Architecture for Human-Robot Interaction

Abstract

In recent years, the ability of intelligent systems to be understood by developers and users has received growing attention. This holds in particular for social robots, which are supposed to act autonomously in the vicinity of human users and are known to raise peculiar, often unrealistic attributions and expectations. However, explainable models that, on the one hand, allow a robot to generate lively and autonomous behavior and, on the other, enable it to provide human-compatible explanations for this behavior are missing. In order to develop such a self-explaining autonomous social robot, we have equipped a robot with own needs that autonomously trigger intentions and proactive behavior, and form the basis for understandable self-explanations. Previous research has shown that undesirable robot behavior is rated more positively after receiving an explanation. We thus aim to equip a social robot with the capability to automatically generate verbal explanations of its own behavior, by tracing its internal decision-making routes. The goal is to generate social robot behavior in a way that is generally interpretable, and therefore explainable on a socio-behavioral level increasing users' understanding of the robot's behavior. In this article, we present a social robot interaction architecture, designed to autonomously generate social behavior and self-explanations. We set out requirements for explainable behavior generation architectures and propose a socio-interactive framework for behavior explanations in social human-robot interactions that enables explaining and elaborating according to users' needs for explanation that emerge within an interaction. Consequently, we introduce an interactive explanation dialog flow concept that incorporates empirically validated explanation types. These concepts are realized within the interaction architecture of a social robot, and integrated with its dialog processing modules. We present the components of this interaction architecture and explain their integration to autonomously generate social behaviors as well as verbal self-explanations. Lastly, we report results from a qualitative evaluation of a working prototype in a laboratory setting, showing that (1) the robot is able to autonomously generate naturalistic social behavior, and (2) the robot is able to verbally self-explain its behavior to the user in line with users' requests.

Causal-Based Approaches to Explain and Learn from Self-Extension—A Review

“…If it can make the person understand what it intends to do through explanations, the robot may truly gain the trust of the people. A robot endowed with these mechanisms is referred to as an explainable autonomous robot (XAR) [9,25]. The concept of XAR is closely linked to that of explainable Artificial Intelligence (XAI), but there is an important difference.…”

“…This research has served as the basis for proposing explanatory frameworks in robotics, which thus emphasise the social-interactive and human behavioural aspects of explanation. Stange et al [9] propose a framework where several types of memories coexist (working memory, episodic memory). A state machine is encoded as an Interaction model, allowing it to recognize and keep track of the human-robot interaction context.…”

“…Thus, socially aware robots are defined here as proactive machines that predict, anticipate, evaluate, and learn from their social context to adapt their behaviour to that context. Ultimately, a socially aware robot must be perceived as trustworthy by humans, a condition that requires transparency, self-explication, and legibility in its behaviour [9][10][11]. However, to be socially aware is to be self-understanding.…”

¿Tramposo e injusto? Entonces, es humano. Robots sociales educativos y ética sintética

“…La investigación actual aboga por una mayor transparencia y explicación sobre los principios del diseño de la robótica, lo que ayudaría a los usuarios a construir modelos mentales más precisos (Stange et al, 2022). Los robots sociales están diseñados para tener un comportamiento más o menos autónomo, sin embargo, la autonomía aumenta la incertidumbre y la imprevisibilidad, por lo que la transparencia y la explicación centrada en el usuario co-bran aún mayor importancia: qué está haciendo el robot y por qué, qué hará a continuación, cuáles son sus capacidades, intenciones y limitaciones situacionales, cuándo y por qué el robot falla al realizar acciones específicas y cómo corregir errores son aspectos esenciales.…”

Effects of Referring to Robot vs. User Needs in Self-Explanations of Undesirable Robot Behavior

“…Aiming at generating a natural and comprehensible basis for lively behavior, social robots such as Kismet [3] or AIBO [7] are often equipped with a motivational system inspired by animal behavior and loosely based on Maslow's theory of behavioral drives [14]. Likewise, Stange et al [18] propose dynamically changing 'needs' that are mapped to certain actions in a three-layered architecture for generating needs-based social robot behavior [18]. Arguably, for social robots it is similarly important to observe and react to the needs of an user, e.g.…”