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Abstract Understanding human trust in machine partners has become imperative due to the widespread use of intelligent machines in a variety of applications and contexts. The aim of this paper is to investigate whether human-beings trust a social robot—i.e. a human-like robot that embodies emotional states, empathy, and non-verbal communication—differently than other types of agents. To do so, we adapt the well-known economic trust-game proposed by Charness and Dufwenberg (2006) to assess whether receiving a promise from a robot increases human-trust in it. We find that receiving a promise from the robot increases the trust of the human in it, but only for individuals who perceive the robot very similar to a human-being. Importantly, we observe a similar pattern in choices when we replace the humanoid counterpart with a real human but not when it is replaced by a computer-box. Additionally, we investigate participants’ psychophysiological reaction in terms of cardiovascular and electrodermal activity. Our results highlight an increased psychophysiological arousal when the game is played with the social robot compared to the computer-box. Taken all together, these results strongly support the development of technologies enhancing the humanity of robots.

Verbal communication is an expanding field in robotics showing a significant increase in both the industrial and research field. The application of verbal communication in robotics aims to reach a natural human-like interaction with robots. In this study, we investigated how salient terms related to verbal communication in robotics have evolved over the years, what are the topics that recur in the related literature, and what are their trends. The study is based on a computational linguistic analysis conducted on a database of 7,435 scientific publications over the last 2 decades. This comprehensive dataset was extracted from the Scopus database using specific key-words. Our results show how relevant terms of verbal communication evolved, which are the main coherent topics and how they have changed over the years. We highlighted positive and negative trends for the most coherent topics and the distribution over the years for the most significant ones. In particular, verbal communication resulted in being highly relevant for social robotics. Potentially, achieving natural verbal communication with a robot can have a great impact on the scientific, societal, and economic role of robotics in the future.

Humanoids have been created for assisting or replacing humans in many applications, providing encouraging results in contexts where social and emotional interaction is required, such as healthcare, education, and therapy. Bioinspiration, that has often guided the design of their bodies and minds, made them also become excellent research tools, probably the best platform by which we can model, test, and understand the human mind and behavior. Driven by the aim of creating a believable robot for interactive applications, as well as a research platform for investigating human cognition and emotion, we are constructing a new humanoid social robot: Abel. In this paper, we discussed three of the fundamental principles that motivated the design of Abel and its cognitive and emotional system: hyper-realistic humanoid aesthetics, human-inspired emotion processing, and human-like perception of time. After reporting a brief state-of-the-art on the related topics, we present the robot at its stage of development, what are the perspectives for its application, and how it could satisfy the expectations as a tool to investigate the human mind, behavior, and consciousness.

A socially intelligent robot must be capable to extract meaningful information in real time from the social environment and react accordingly with coherent human-like behavior. Moreover, it should be able to internalize this information, to reason on it at a higher level, build its own opinions independently, and then automatically bias the decision-making according to its unique experience. In the last decades, neuroscience research highlighted the link between the evolution of such complex behavior and the evolution of a certain level of consciousness, which cannot leave out of a body that feels emotions as discriminants and prompters. In order to develop cognitive systems for social robotics with greater human-likeliness, we used an “understanding by building” approach to model and implement a well-known theory of mind in the form of an artificial intelligence, and we tested it on a sophisticated robotic platform. The name of the presented system is SEAI (Social Emotional Artificial Intelligence), a cognitive system specifically conceived for social and emotional robots. It is designed as a bio-inspired, highly modular, hybrid system with emotion modeling and high-level reasoning capabilities. It follows the deliberative/reactive paradigm where a knowledge-based expert system is aimed at dealing with the high-level symbolic reasoning, while a more conventional reactive paradigm is deputed to the low-level processing and control. The SEAI system is also enriched by a model that simulates the Damasio’s theory of consciousness and the theory of Somatic Markers. After a review of similar bio-inspired cognitive systems, we present the scientific foundations and their computational formalization at the basis of the SEAI framework. Then, a deeper technical description of the architecture is disclosed underlining the numerous parallelisms with the human cognitive system. Finally, the influence of artificial emotions and feelings, and their link with the robot’s beliefs and decisions have been tested in a physical humanoid involved in Human–Robot Interaction (HRI).

Humans have an innate tendency to anthropomorphize surrounding entities and have always been fascinated by the creation of machines endowed with human-inspired capabilities and traits. In the last few decades, this has become a reality with enormous advances in hardware performance, computer graphics, robotics technology, and artificial intelligence. New interdisciplinary research fields have brought forth cognitive robotics aimed at building a new generation of control systems and providing robots with social, empathetic and affective capabilities. This paper presents the design, implementation, and test of a human-inspired cognitive architecture for social robots. State-of-the-art design approaches and methods are thoroughly analyzed and discussed, cases where the developed system has been successfully used are reported. The tests demonstrated the system’s ability to endow a social humanoid robot with human social behaviors and with in-silico robotic emotions.

In this work, we present an unobtrusive and non-invasive perception framework based on the synergy between two main acquisition systems: the Touch-Me Pad, consisting of two electronic patches for physiological signal extraction and processing; and the Scene Analyzer, a visual-auditory perception system specifically designed for the detection of social and emotional cues. It will be explained how the information extracted by this specific kind of framework is particularly suitable for social robotics applications and how the system has been conceived in order to be used in human-robot interaction scenarios.