A better understanding of how humans perceive robot personality variables could enable the design of more socially acceptable robots. In this exploratory study, we examined whether manipulations of an iCub robot’s voice and movements affected human participants’ perceptions of the robot’s personality. We programmed the robot to behave in different ways during a teaching scenario in which it played either a teaching, learning, or collaborative role, shown in recorded videos of human–robot interactions. A total of 240 participants in an Amazon Mechanical Turk study watched these videos and completed a series of questionnaires assessing their perceptions of the robot. Participants perceived the iCub as more extroverted when it spoke faster, with a higher pitch, and performed larger-amplitude movements. It was determined that participants’ personality dimensions were more influential in their perceptions of the robot’s TIPI and RoSAS personality dimensions than the robot’s social role and personality manipulations. Participants’ self-rated extroversion, emotional stability, and conscientiousness repeatedly appeared as significant factors affecting their perceptions of the robot’s personality. Interestingly, we observed strong perceiver effects, whereby participants’ perceptions of the robot’s personality traits were correlated with their own self-rated personality traits.

To be able to learn effectively, robots sometimes will need to select more suitable human teachers. We propose an attribute in human teachers for robots that learn through visual observations, namely human teachers’ awareness of and attention to the robot’s visual capabilities and constraints, and explore how it affects robot learning outcomes. In an in-person experiment involving 72 participants who taught three physical tasks to an iCub humanoid robot, we manipulated teachers’ awareness of the robot’s visual constraints by offering the visual perspective of the robot in one of the experimental conditions. Participants who were able to see the robot’s vision output paid increased attention to ensuring task objects were visible to the robot when providing demonstrations of physical tasks. This emphasis on attention to the robot’s view resulted in better learning outcomes for the robot, as indicated by lower perception error rates and higher learning scores. This study contributes to understanding factors in human teachers that lead to better learning outcomes for robots.

For social robots to succeed in places such as homes, they must learn new skills from various people and act in a manner desirable to different users. We introduce a novel biologically inspired approach for robot learning through program-level imitation, inspired by the way primates, including humans, understand and perform complex actions. Our approach enables robots to discover the hierarchical structure of tasks by identifying sequential regularities and sub-goals from diverse human demonstrations. To do so, human-provided demonstrations, which can be obtained by a robot through different modalities (such as kinesthetic teaching, behavioural observation, and verbal instruction), are processed by an algorithm that discovers multiple possibilities for arranging observed sub-goals to achieve a final goal. Prior to acting, the available sequences are evaluated based on user-defined criteria, through mental simulation of the task by the robot, to find the optimal sequence of actions. As a proof-of-concept, we implemented our system on an iCub humanoid robot and present here how our method allowed the robot to adapt its action sequences for task execution when starting the task from different states, incorporating user preference for finishing the task as fast as possible. Our envisaged system is meant to accommodate variations in human teaching styles and is expected to help a robot perform tasks with greater flexibility and efficiency. This work contributes by proposing a framework for robots to learn from humans at an abstract level, opening the way to more adaptable and intelligent robotic assistants in everyday tasks.

HRI research has evolved to take a broader, more inclusive view of how culture influences our interaction with robots. As we delve deeper into cultural integration in HRI, it has become evident that while integrating cultural aspects offers new opportunities, it requires careful consideration due to the heightened sensitivity to the fluid nature of cultural dynamics. Our study focuses on a particular case and examines the role of context and personal preferences in a restaurant setting. We investigate how preferences for cross-cultural greetings performed by a humanoid robot can change based on the restaurant theme and describe what factors influence these preferences by looking at two different groups who participated based on different ethnic greetings. Our study reveals insight into how ethnicity, percentage of life lived in Western countries, personality variations, and implementation of cultural aspects influence the likability of robotic greeting gestures. Our investigations highlight the complexity of creating culturally adaptive robots that demonstrate the cultural norms and gestures that align with the expectations of the respective cultural groups.

To be successful, robots that can learn new tasks from humans should interact effectively with them while being trained, and humans should be able to trust the robots’ abilities after teaching. Typically, when human learners make mistakes, their teachers tolerate those errors, especially when students exhibit acceptable progress overall. But how do errors and appearance of a trainee robot affect human teachers’ trust while the robot is generally improving in performing a task? First, an online survey with 173 participants investigated perceived severity of robot errors in performing a cooking task. These findings were then used in an interactive online experiment with 138 participants, in which the participants were able to remotely teach their food preparation preferences to trainee robots with two different appearances. Compared with an untidy-looking robot, a tidy-looking robot was rated as more professional, without impacting participants’ trust. Furthermore, while larger errors at the end of iterative training had a greater impact, even a small error could significantly reduce trust in a trainee robot performing the domestic physical task of food preparation, regardless of the robot’s appearance. The present study extends human–robot interaction knowledge about teachers’ perception of trainee robots, particularly when teachers observe them accomplishing domestic physical tasks.

When humans make a mistake, they often try to employ some strategies to manage the situation and possibly mitigate the negative effects of the mistake. Robots that operate in the real world will also make errors and therefore might benefit from such recovery strategies. In this work, we studied how different verbal expression strategies of a trainee humanoid robot when committing an error after learning a task influence participants’ intention to use it. We performed a virtual experiment in which the expression modes of the robot were as follows: (1) being silent; (2) verbal expression but ignoring any errors; or (3) verbal expression while mentioning any error by apologizing, as well as acknowledging and justifying the error. To simulate teaching, participants remotely demonstrated their preferences to the robot in a series of food preparation tasks; however, at the very end of the teaching session, the robot made an error (in two of the three experimental conditions). Based on data collected from 176 participants, we observed that, compared to the mode where the robot remained silent, both modes where the robot utilized verbal expression could significantly enhance participants’ intention to use the robot in the future if it made an error in the last practice round. When no error occurred at the end of the practice rounds, a silent robot was preferred and increased participants’ intention to use.

When human students practise new skills with a teacher, they often display nonverbal behaviours (e.g., head and limb movements, gaze, etc.) to communicate their level of understanding and expressing their interest in the task. Similarly, a student robot's capability to provide human teachers with social signals to express its internal state might improve learning outcomes. This could also lead to a more successful social interactions between intelligent robots and human teachers. However, to design successful nonverbal communication for a robot, we first need to understand how human teachers interpret such nonverbal cues when watching a trainee robot practising a task. Therefore, in this paper, we study the effects of different gaze behaviours as well as manipulating speed and smoothness of arm movement on human teachers' perception of a robot's (a) confidence, (b) eagerness to learn, and (c) attention to the task. In an online experiment, we asked the 167 participants (as teachers) to rate the behaviours of a trainee robot in the context of learning a physical task. The results suggest that splitting the robot's gaze between the teacher and the task not only affects the perceived attention, but can also make the robot appear to be more eager to learn. Furthermore, perceptions of all three attributes tested were systematically affected by varying parameters of the robot's arm movement trajectory while performing task actions.

It is anticipated that intelligent robots will gain the ability to learn from humans how to perform tasks, and will assist them in many contexts such as with household chores in the near future; therefore, people should have the confidence to trust these robots after teaching them how to do a task. Like most machines, robots may sometimes behave in an erroneous manner and such errors can easily undermine trust in the robots, depending on their severity. Nevertheless, when a robot has been taught a task by humans, we hypothesize that the teachers may ignore small mistakes made by the robot, if it shows significant improvements while practising the task. We first conducted a study with 173 participants in which the perceived severity of different robot errors in a household chore (preparing food) was investigated. We then used the results to create scenarios of different levels of severity and conducted a second study with 138 participants to investigate the impact of error severity on trust. Participants remotely taught their preferences in food preparation tasks to robots. Over several practice rounds, robots’ behaviour improved, but the robots made either (a) no errors, (b) a small, or (c) a big error at the end, depending on the experimental condition. Small errors significantly affected trust and big errors had an even more adverse impact. Trust in the robot was found to be correlated with personality traits of the participants as well as with their disposition to trust other people.