Kheng Lee Koay

On reviewing the literature regarding acceptance and trust in human-robot interaction (HRI), there are a number of open questions that needed to be addressed in order to establish effective collaborations between humans and robots in real-world applications. In particular, we identified four principal open areas that should be investigated to create guidelines for the successful deployment of robots in the wild. These areas are focused on: (1) the robot’s abilities and limitations; in particular when it makes errors with different severity of consequences, (2) individual differences, (3) the dynamics of human-robot trust, and (4) the interaction between humans and robots over time. In this paper, we present two very similar studies, one with a virtual robot with human-like abilities, and one with a Care-O-bot 4 robot. In the first study, we create an immersive narrative using an interactive storyboard to collect responses of 154 participants. In the second study, 6 participants had repeated interactions over three weeks with a physical robot. We summarise and discuss the findings of our investigations of the effects of robots’ errors on people’s trust in robots for designing mechanisms that allow robots to recover from a breach of trust. In particular, we observed that robots’ errors had greater impact on people’s trust in the robot when the errors were made at the beginning of the interaction and had severe consequences. Our results also provided insights on how these errors vary according to the individuals’ personalities, expectations and previous experiences.

When studying the use of assistive robots in home environments, and especially how such robots can be personalised to meet the needs of the resident, key concerns are issues related to behaviour verification, behaviour interference and safety. Here, personalisation refers to the teaching of new robot behaviours by both technical and non-technical end users. In this article, we consider the issue of behaviour interference caused by situations where newly taught robot behaviours may affect or be affected by existing behaviours and thus, those behaviours will not or might not ever be executed. We focus in particular on how such situations can be detected and presented to the user. We describe the human–robot behaviour teaching system that we developed as well as the formal behaviour checking methods used. The online use of behaviour checking is demonstrated, based on static analysis of behaviours during the operation of the robot, and evaluated in a user study. We conducted a proof-of-concept human–robot interaction study with an autonomous, multi-purpose robot operating within a smart home environment. Twenty participants individually taught the robot behaviours according to instructions they were given, some of which caused interference with other behaviours. A mechanism for detecting behaviour interference provided feedback to participants and suggestions on how to resolve those conflicts. We assessed the participants’ views on detected interference as reported by the behaviour teaching system. Results indicate that interference warnings given to participants during teaching provoked an understanding of the issue. We did not find a significant influence of participants’ technical background. These results highlight a promising path towards verification and validation of assistive home companion robots that allow end-user personalisation.

This paper presents a proof of concept prototype study for domestic home robot companions, using a narrative-based methodology based on the principles of immersive engagement and fictional enquiry, creating scenarios which are inter-connected through a coherent narrative arc, to encourage participant immersion within a realistic setting. The aim was to ground human interactions with this technology in a coherent, meaningful experience. Nine participants interacted with a robotic agent in a smart home environment twice a week over a month, with each interaction framed within a greater narrative arc. Participant responses, both to the scenarios and the robotic agents used within them are discussed, suggesting that the prototyping methodology was successful in conveying a meaningful interaction experience.

Robot companions are starting to become more common and people are becoming more familiar with devices such as Google Home, Alexa or Pepper, one must wonder what is the optimum way for people to control their devices? This paper presents an investigation into how much direct control people want to have of their robot companion and how dependent this is on the criticality of the tasks the robot performs. A live experiment was conducted in the University of Hertfordshire Robot House, with a robot companion performing four different type of tasks. The four tasks were: booking a doctor’s appointment, helping the user to build a Lego character, doing a dance with the user, and carrying biscuits for the user. The selection of these tasks was based on our previous research to define tasks which were relatively high and low in criticality. The main goal of the study was to find what level of direct control over their robot participants have, and if this was dependent on the criticality of the task performed by the robot. Fifty people took part in the study, and each experienced every task in a random order. Overall, it was found that participants’ perception of control was higher when the robot was performing a task in a semi-autonomous mode. However, for the task “carrying biscuits”, although participants perceived to be more in control with the robot performing the task in a semi-autonomous mode, they actually preferred to have the robot performing the task automatically (where they felt less in control). The results also show that, for the task “booking a doctor’s appointment”, considered to be the most critical of all four tasks, participants did not prefer that the robot chose the date of the appointment as they felt infantilised.

Humans should be able to trust that they can safely interact with their home companion robot. However, robots can exhibit occasional mechanical, programming or functional errors. We hypothesise that the severity of the consequences and the timing of a robot’s different types of erroneous behaviours during an interaction may have different impacts on users’ attitudes towards a domestic robot. First, we investigated human users’ perceptions of the severity of various categories of potential errors that are likely to be exhibited by a domestic robot. Second, we used an interactive storyboard to evaluate participants’ degree of trust in the robot after it performed tasks either correctly, or with ‘small’ or ‘big’ errors. Finally, we analysed the correlation between participants’ responses regarding their personality, predisposition to trust other humans, their perceptions of robots, and their interaction with the robot. We conclude that there is correlation between the magnitude of an error performed by a robot and the corresponding loss of trust by the human towards the robot. Moreover we observed that some traits of participants’ personalities (conscientiousness and agreeableness) and their disposition of trusting other humans (benevolence) significantly increased their tendency to trust a robot more during an emergency scenario.