text: To design trustworthy robots, we need to understand the impact factors of trust: people's attitudes, experience, and characteristics; the robot's physical design, reliability, and performance; a task's specification and the circumstances under which it is to be performed, e.g. at leisure or under time pressure. As robots are used for a wide variety of tasks and applications, robot designers ought to be provided with evidence and guidance, to inform their decisions to achieve safe, trustworthy and efficient human-robot interactions. In this work, the impact factors of trust in a collaborative manufacturing scenario are studied by conducting an experiment with a real robot and participants where a physical object was assembled and then disassembled. Objective and subjective measures were employed to evaluate the development of trust, under faulty and non-faulty robot conditions, and the effect of previous experience with robots, and personality traits. Our findings highlight differences when compared to other, more social, scenarios with robotic assistants (such as a home care assistant), in that the condition (faulty or not) does not have a significant impact on the human's perception of the robot in terms of human-likeliness, likeability, trustworthiness, and even competence. However, personality and previous experience do have an effect on how the robot is perceived by participants, even though that is relatively small.
  @cite_35: In all human-robot interaction, trust is an important element to consider because the presence or absence of trust certainly impacts the ultimate outcome of that interaction. Limited research exists that delineates the development and maintenance of this trust in various operational contexts. Our own prior research has investigated theoretical and empirically supported antecedents of human-robot trust. Here, we describe progress to date relating to the development of a comprehensive human-robot trust model based on our ongoing program of research.
  @cite_31: 
  @cite_29: Strategies are necessary to mitigate the impact of unexpected behavior in collaborative robotics, and research to develop solutions is lacking. Our aim here was to explore the benefits of an affective interaction, as opposed to a more efficient, less error prone but non-communicative one. The experiment took the form of an omelet-making task, with a wide range of participants interacting directly with BERT2, a humanoid robot assistant. Having significant implications for design, results suggest that efficiency is not the most important aspect of performance for users; a personable, expressive robot was found to be preferable over a more efficient one, despite a considerable trade off in time taken to perform the task. Our findings also suggest that a robot exhibiting human-like characteristics may make users reluctant to 'hurt its feelings'; they may even lie in order to avoid this.
  @cite_7: Previous work has shown that gestural behaviors affect anthropomorphic inferences about artificial communicators such as virtual agents. In an experiment with a humanoid robot, we investigated to what extent gesture would affect anthropomorphic inferences about the robot. Particularly, we examined the effects of the robot's hand and arm gestures on the attribution of typically human traits, likability of the robot, shared reality, and future contact intentions after interacting with the robot. For this, we manipulated the non-verbal behaviors of the humanoid robot in three experimental conditions: (1) no gesture, (2) congruent gesture, and (3) incongruent gesture. We hypothesized higher ratings on all dependent measures in the two gesture (vs. no gesture) conditions. The results confirm our predictions: when the robot used gestures during interaction, it was anthropomorphized more, participants perceived it as more likable, reported greater shared reality with it, and showed increased future contact intentions than when the robot gave instructions without using gestures. Surprisingly, this effect was particularly pronounced when the robot's gestures were partly incongruent with speech. These findings show that communicative non-verbal behaviors in robotic systems affect both anthropomorphic perceptions and the mental models humans form of a humanoid robot during interaction.
summary: As mentioned in @cite_31 , the ability of the humans (e.g. previous experience and training), their characteristics (e.g. demographics, personality, attitudes), the performance of the robot, and its attributed characteristics (e.g. anthropomorphism), as well as the environment (e.g. communication channels) and task nature (e.g. difficulty), have been studied as factors to contribute to the trust development in human--robot interactions. Also, a segment of research in human-robot interaction has focused on the influence of the robot's design (e.g. anthropomorphism, communication features, gestures) in the development of trust, as robot characteristics influence it the most, compared to environmental and human-related factors @cite_35 . @cite_7 and @cite_29 have studied whether people trust expressive robots more or not, even when they make mistakes. These studies found that co-verbal gestures and more expressiveness caused the robots to be perceived more human-like and likeable, increasing future contact or interaction intentions, even though the mistakes affected a successful performance during the tasks.