Exploring Learning Techniques for Developing Socially-Aware Service Robots: Best Practices for Social Comfort

In the past few years, there has been an increase in commercial and research focus on service robots operating in daily surroundings. These machines are anticipated to function independently in busy settings, enhancing movement efficiency and safety parameters, as well as social acceptance. Expanding conventional path planning modules to include socially aware criteria, while sustaining speedy algorithms that can adapt to human behavior without causing distress, presents a significant challenge. To address this challenge, learning methods have gained significant relevance. Among the various techniques, deep reinforcement learning, end-to-end, and inverse reinforcement learning have been the most promising. However, it is difficult to determine which techniques are superior, and sometimes, developers may obtain poor results due to inadequate data or experimental procedures during the learning stage. Therefore, it is essential to evaluate and discuss the best practices and options for an effective training stage that can improve results. As we are specifically referring to social robots, the evaluation of results should take into consideration social comfort as a key factor.