A Systematic Literature Review of Decision-Making and Control Systems for Autonomous and Social Robots

In the last years, considerable research has been carried out to develop robots that can improve our quality of life during tedious and challenging tasks. In these contexts, robots operating without human supervision open many possibilities to assist people in their daily activities. When autonomous robots collaborate with humans, social skills are necessary for adequate communication and cooperation. Considering these facts, endowing autonomous and social robots with decision-making and control models is critical for appropriately fulfiling their initial goals. This manuscript presents a systematic review of the evolution of decision-making systems and control architectures for autonomous and social robots in the last three decades. These architectures have been incorporating new methods based on biologically inspired models and Machine Learning to enhance these systems’ possibilities to developed societies. The review explores the most novel advances in each application area, comparing their most essential features. Additionally, we describe the current challenges of software architecture devoted to action selection, an analysis not provided in similar reviews of behavioural models for autonomous and social robots. Finally, we present the future directions that these systems can take in the future.

A Critical Analysis of Industrial Human-Robot Communication and Its Quest for Naturalness Through the Lens of Complexity Theory

Human-robot communication is one of the actively researched fields to enable efficient and seamless collaboration between a human and an intelligent industrial robotic system. The field finds its roots in human communication with the aim to achieve the “naturalness” inherent in the latter. Industrial human-robot communication pursues communication with simplistic commands and gestures, which is not representative of an uncontrolled real-world industrial environment. In addition, naturalness in communication is a consequence of its dynamism, typically ignored as a design criterion in industrial human-robot communication. Complexity Theory-based natural communication models allow for a more accurate representation of human communication which, when adapted, could also benefit the field of human-robot communication. This paper presents a perspective by reviewing the state of human-robot communication in industrial settings and then presents a critical analysis of the same through the lens of Complexity Theory. Furthermore, the work identifies research gaps in the aforementioned field, fulfilling which, would propel the field towards a truly natural form of communication. Finally, the work briefly discusses a general framework that leverages the experiential learning of data-based techniques and naturalness of human knowledge.