SONAR: An Adaptive Control Architecture for Social Norm Aware Robots

Abstract

Recent advances in robotics and artificial intelligence have made it necessary or desired for humans to get involved in interactions with social robots. A key factor for the human acceptance of these robots is their awareness of environmental and social norms. In this paper, we introduce SONAR (for SOcial Norm Aware Robots), a novel robot-agnostic control architecture aimed at enabling social agents to autonomously recognize, act upon, and learn over time social norms during interactions with humans. SONAR integrates several state-of-the-art theories and technologies, including the belief-desire-intention (BDI) model of reasoning and decision making for rational agents, fuzzy logic theory, and large language models, to support adaptive and norm-aware autonomous decision making. We demonstrate the feasibility and applicability of SONAR via real-life experiments involving human-robot interactions (HRI) using a Nao robot for scenarios of casual conversations between the robot and each participant. The results of our experiments show that our SONAR implementation can effectively and efficiently be used in HRI to provide the robot with environmental and social and norm awareness. Compared to a robot with no explicit social and norm awareness, introducing social and norm awareness via SONAR results in interactions that are perceived as more positive and enjoyable by humans, as well as in higher perceived trust in the social robot. Moreover, we investigate, via computer-based simulations, the extent to which SONAR can be used to learn and adapt to the social norms of different societies. The results of these simulations illustrate that SONAR can successfully learn adequate behaviors in a society from a relatively small amount of data. We publicly release the source code of SONAR, along with data and experiments logs.