Recent studies have revealed the key importance of modelling personality in robots to improve interaction quality by empowering them with social-intelligence capabilities. Most research relies on verbal and non-verbal features related to personality traits that are highly context-dependent. Hence, analysing how humans behave in a given context is crucial to evaluate which of those social cues are effective. For this purpose, we designed an assistive memory game, in which participants were asked to play the game obtaining support from an introvert or extroverted helper, whether from a human or robot. In this context, we aim to (i) explore whether selective verbal and non-verbal social cues related to personality can be modelled in a robot, (ii) evaluate the efficiency of a statistical decision-making algorithm employed by the robot to provide adaptive assistance, and (iii) assess the validity of the similarity attraction principle. Specifically, we conducted two user studies. In the human–human study (N=31), we explored the effects of helper’s personality on participants’ performance and extracted distinctive verbal and non-verbal social cues from the human helper. In the human–robot study (N=24), we modelled the extracted social cues in the robot and evaluated its effectiveness on participants’ performance. Our findings showed that participants were able to distinguish between robots’ personalities, and not between the level of autonomy of the robot (Wizard-of-Oz vs fully autonomous). Finally, we found that participants achieved better performance with a robot helper that had a similar personality to them, or a human helper that had a different personality.

The Covid-19 pandemic has stimulated the use of social robots in front-office services. However, some initial applications yielded disappointing results, as managers were unaware of the level of development of the robots’ artificial intelligence systems. This study proposes to adapt the Almere model to estimate the technological acceptance of service robots, which express their gender and personality, whilst assisting consumers. A 2 × 2 (two genders vs. two personalities) between-subjects experiment was conducted with 219 participants. Model estimation with Structural Equation Modelling confirmed seven out of eight hypotheses, and all four scenarios were estimated with Ordinary Least Squares, showing that robot gender and personality affected their technological acceptance.

In recent years, the rapid ageing of the population, a longer life expectancy and elderly people’s desire to live independently are social changes that put pressure on healthcare systems. This context is boosting the demand for companion and entertainment social robots on the market and, consequently, producers and distributors are interested in knowing how these social robots are accepted by consumers. Based on technology acceptance models, a parsimonious model is proposed to estimate the intention to use this new advanced social robot technology and, in addition, an analysis is performed to determine how consumers’ gender and rational thinking condition the precedents of the intention to use. The results show that gender differences are more important than suggested by the literature. While women gave greater social influence and perceived enjoyment as the main motives for using a social robot, in contrast, men considered their perceived usefulness to be the principal reason and, as a differential argument, the ease of use. Regarding the reasoning system, the most significant differences occurred between heuristic individuals, who stated social influence as the main reason for using a robot, and the more rational consumers, who gave ease of use as a differential argument.

Socially assistive robots have the potential to augment and enhance therapist’s effectiveness in repetitive tasks such as cognitive therapies. However, their contribution has generally been limited as domain experts have not been fully involved in the entire pipeline of the design process as well as in the automatisation of the robots’ behaviour. In this article, we present aCtive leARning agEnt aSsiStive bEhaviouR (CARESSER), a novel framework that actively learns robotic assistive behaviour by leveraging the therapist’s expertise (knowledge-driven approach) and their demonstrations (data-driven approach). By exploiting that hybrid approach, the presented method enables in situ fast learning, in a fully autonomous fashion, of personalised patient-specific policies. With the purpose of evaluating our framework, we conducted two user studies in a daily care centre in which older adults affected by mild dementia and mild cognitive impairment (N = 22) were requested to solve cognitive exercises with the support of a therapist and later on of a robot endowed with CARESSER. Results showed that: (i) the robot managed to keep the patients’ performance stable during the sessions even more so than the therapist; (ii) the assistance offered by the robot during the sessions eventually matched the therapist’s preferences. We conclude that CARESSER, with its stakeholder-centric design, can pave the way to new AI approaches that learn by leveraging human–human interactions along with human expertise, which has the benefits of speeding up the learning process, eliminating the need for the design of complex reward functions, and finally avoiding undesired states.