Enhancing Humans Trust in Robots through Explanations

Trust in Robots Versus Trust in Humans

Developments in the assistive capabilities of robots have allowed them to be more prevalent across domains from manufacturing to healthcare to education. To achieve more efficient and fluent collaborations between robots and humans, it has become necessary to explore how humans trust robot partners during collaborations. Current measurements of human trust in a robot partner typically rely on reflective, subjective survey measures, which are difficult to incorporate and update in the robot’s decision-making models for real-time deployment. To address this challenge, we aim to identify potential objective measures of trust, which can be incorporated into the robot’s decision-making process. We examine physiological data (Electrodermal Activity (EDA), Skin Temperature (TEMP), and Blood Volume Pulse (BVP)) and facial expression in an in-person ( \(n=30\) ) human–robot collaboration study and report the effect of trust on these objective measures. The results indicate that the participants who distrusted the robot partner experienced higher EDA, higher TEMP, and lower BVP variations than participants who trusted the robot. For facial expression, participants who distrusted the robot had more intense facial expressions than participants who trusted the robot. Ultimately, these findings can be used to further research on developing a real-time model of human trust in robots.

Understanding and modelling how humans develop and maintain trust in robots is crucial for ensuring appropriate trust calibration during Human-Robot Interaction (HRI). This paper presents a mathematical model that simulates a three-layered framework of trust, encompassing dispositional, situational and learned trust. This framework aims to estimate human trust in robots during real-time interactions. Our trust model was tested and validated in an experimental setting where participants engaged in a collaborative trust game with a robot over four interactive sessions. Results from mixed-model analysis revealed that both the Trust Perception Score (TPS) and interaction session significantly predicted the Trust Modeled Score (TMS), explaining a substantial portion of the variance in TMS. Statistical analysis demonstrated significant differences in trust across sessions, with mean trust scores showing a clear increase from the first to the final session. Additionally, we observed strong correlations between situational and learned trust layers, demonstrating the model’s ability to capture dynamic trust evolution. These findings underscore the potential of this model in developing adaptive robotic behaviours that can respond to changes in human trust levels, ultimately advancing the design of robotic systems capable of real-time trust calibration.

Domestic service robots are nowadays widely available on the consumer market. As such, robots have begun entering people’s homes and daily lives. However, it seems that the dissemination of domestic robots has not happened as easily and widespread as it was anticipated in the first place. Little is known about the reasons why because long-term studies of ordinary people using real robots in their homes are rare. To better understand how people interact, use and accept domestic robots, studies of human-robot interaction require ecologically valid settings and the user and their needs have to come into the focus. In this dissertation, we propose to investigate the dynamics of human-robot interaction in domestic environments. We first explore the field by means of a 6-month ethnographic study of nine households. We provided each of the households with a Roomba vacuum cleaning robot. Our motivation is to understand long-term acceptance and to identify factors that can promote and hinder the integration of a domestic service robot in different types of households. We would like to find out how people’s perception of the robot, and the way they interact with it and use it, evolve over time. Furthermore, as social factors were highlighted in previous studies on technology adoption in homes, we shed light on to what extent people view Roomba and other types of domestic robots as a social entity and to what extent they anthropomorphize it. Findings of this research can be used to guide the design of user-oriented robots that have the potential to lastingly become a valuable part within the home ecology. Then, we pursue the idea of developing our own domestic robot prototype that could be used in a household with children. We imagine a playful robot that aims to motivate young children to tidy up their toys. In a first evaluation of the robot in 14 family homes, we study the effect of a proactive and reactive robot behavior on children’s interaction with the robot and their motivation to tidy up. A follow-up experiment explores the possibility to sustain children’s engagement by manipulating the robot’s behavior in such way that it appears unexpected. We further investigate how far this influences children’s perception of the robot in terms of anthropomorphism. Our findings emphasize the importance of research in ecologically valid settings in order to obtain a better understanding of human-robot interaction, advance further the design of user-oriented robots and foster the long-term acceptance of these devices.

Error handling in multimodal voice-enabled interfaces of tour-guide robots using graphical models

The trust levels that participants report on questionnaires may be biased by the participant’s motivation to appear like a trusting person. Few studies have found participants’ explicit trust to be inconsistent with their implicit trust. According to social cognition theory, the inconsistency between explicit and implicit trust indicates the importance of an implicit measurement of trust. However, trusting has been socially expected in previous research, which facilitates participants’ motivation to deliberate their responses on questionnaires. As a result, the inconsistency between explicit and implicit trust that previous research revealed may be context dependent. This study examined this possibility by investigating college students’ implicit trust, implicit distrust and explicit trust toward eight professions. The study found that 1) college students’ explicit trust did not correlate with their implicit trust; 2) participants’ implicit trust positively correlated with their implicit distrust which indicates that trust and distrust are two separate but linked constructs; 3) participants’ trust behavior tendencies can be predicted by implicit trust and implicit distrust. We conclude that the automatic attitude activation task is valid in implicit trust measurement, and implicit and explicit measurements of trust can and should complement each other.

We are approaching a future where social robots will progressively become widespread in many aspects of our daily lives, including education, healthcare, work, and personal use. All such practical applications require that humans and robots collaborate in human environments, where social interaction is unavoidable. Along with verbal communication, successful social interaction is closely coupled with nonverbal communication. Humans perform nonverbal communication in an instinctive and adaptive way, with no effort. For robots to be successful in our social landscape, they should therefore engage in social interactions in a human-like manner, with increasing levels of autonomy. Understanding human-human interaction is therefore the most natural guide to designing human-robot interaction interfaces that can be usable and understandable by everyone. The key aim of this special session is to bring forth efforts to understand mechanisms underlying human-human nonverbal communication and introduce novel approaches to the design, development, and evaluation of robotic platforms inspired and driven by those mechanisms. This special session will bring together researchers with diverse backgrounds, ranging from social psychology, cognitive science, machine learning, computer vision, and human-robot interaction, to share innovative ideas, findings, and challenges related to nonverbal communication skills in humans and robots. The special session will foster multidisciplinary discussions among academic and industrial researchers towards improving robots’ nonverbal communication skills and enhancing human trust and acceptance of robots.

Despite the increased interest in automation and the expanded deployment of robots in the construction industry, using robots in a dynamic and unstructured working environment has caused safety concerns in operating construction robots. Improving human–robot interaction (HRI) can increase the adoption of robots on construction sites; for example, increasing trust in robots could help construction workers to accept new technologies. Confidence in operation (or self-efficacy), mental workload, and situational awareness are among other key factors that help such workers to remote operate robots safely. However, construction workers have very few opportunities to practice with robots to build trust, self-efficacy, and situational awareness, as well as resistance against increasing mental workload, before interacting with them on job sites. Virtual reality (VR) could afford a safer place to practice with the robot; thus, we tested if VR-based training could improve these four outcomes during the remote operation of construction robots. We measured trust in the robot, self-efficacy, mental workload, and situational awareness in an experimental study where construction workers remote-operated a demolition robot. Fifty workers were randomly assigned to either VR-based training or traditional in-person training led by an expert trainer. Results show that VR-based training significantly increased trust in the robot, self-efficacy, and situational awareness, compared to traditional in-person training. Our findings suggest that VR-based training can allow for significant increases in beneficial cognitive factors over more traditional methods and has substantial implications for improving HRI using VR, especially in the construction industry.

Due to declined birthrate and the increasing aging population, shortage of caregiving labor force has become a critical issue worldwide. Introducing robotic products could provide an effective way to help older adults’ daily lives. However, previous studies indicated that older adults’ acceptance of robots was lower than younger adults. One possible reason of this lower acceptance of robots might be robot appearance. The Uncanny Valley Phenomenon (UVP) refers to the phenomenon that people rate more positively as robots become more humanlike, but only up to a certain point; as it approaches near-perfect similarity of human appearance, likeability drops and forms an uncanny valley. Nonetheless, evidence for the UVP were mainly from younger adults. We therefore examined whether the UVP is also applicable for older and middle-aged adults in the present study. We also examined whether the acceptance of functions (companion vs. service) would change based on robot appearance, and whether perceived personalities have any relation with the acceptance of robot function. We asked younger (N= 80, age 18-39), middle-aged (N= 87, age 40-59), and older (N= 88, age 60-87) adults to view each picture of a set of robot pictures selected from a totally 84 robots and evaluate their impression on each robot and the intention of use regarding robot functions. UVP was found in younger and middle-aged adults; however, older adults did not show UVP. They preferred humanlike over non-humanlike robots, regardless of robot function. Scores on each personality－except for authoritativeness－showed positive correlations with the acceptance of functions. These findings suggest that the design of assistive robots should take UVP into consideration by customizing robot appearance in accordance with the function provided to different age groups.

Appearance-based localization compares the current image taken from a robot's camera to a set of pre-recorded images in order to estimate the current location of the robot. Such techniques often maintain a graph of images, modeling the dynamics of the image sequence. This graph is used to navigate in the space of images.In this paper we bring a set of techniques together, including Partially-Observable Markov Decision Processes, hierarchical state representations, visual homing, human-robot interactions, and so forth, into the appearance-based approach. Our approach provides a complete solution to the deployment of a robot in a relatively small environment, such as a house, or a work place, allowing the robot to robustly navigate the environment after minimal training.We demonstrate our approach in two environments using a real robot, showing how after a short training session, the robot is able to navigate well in the environment.

Before a future with household robots is really feasible, those robots need to be easily adaptable to novel environments and users, be able to apply previously acquired knowledge, and able to learn from perceiving and interacting with the world and users around them. This thesis proposes a cognitive architecture and a set of underlining methods for automatic knowledge acquisition that will bring household robots one step closer to reality. The thesis was inspired by the cognitive development of babies and subsequently treats the topics: Knowledge representation, Sensory motor integration, Knowledge acquisition & learning and Real world applications. We derived ways to describe and efficiently combine visual information, despite cluttered environments and changing illumination. We propose a method to combine different key-point extractors and to select only a small subset based on their information entropy. Since object appearances can vary in colour, texture and 2D/3D shape, we extract feature vectors from multiple viewpoints of the object and calculate the dominant features, which leads to a more robust object recognition process. This also automatically calculates the most efficient way to store the representative features of unknown objects. This method is similar to the information storage process in humans; an important characteristic of human learning is the pruning of information in the working memory to the most relevant information to be stored in long term memory. In order to detect and segment objects without any prior knowledge on their appearance or their background, we designed a visual-attention module that is able to find the salient parts of an image and clusters them into objects ranked by their saliency. Once these objects are localized, the system must act upon them to efficiently learn their properties. This is achieved by simultaneous exploration and manipulation of the selected - most salient - unknown object. However, such a cognitive system cannot be realized without a robot body, and therefore we designed a simple and affordable robot. Another cognitive ability that a future household robot certainly must have is knowledge accumulation from previous experiences. Consequently, we designed a cognitive architecture that allows a robot that has no pre- insight in the world and its objects to incrementally acquire all its expertise from interaction with the environment. We proposed models of working and long term memory as well as algorithms for novelty detection. Finally, we treat two realized applications for external parties, based on our algorithms: Bin-picking by an industrial robot and action detection in ceiling mounted cameras in elderly homes.

Human factors and human-centered design philosophy are highly desired in today's robotics applications such as human-robot interaction (HRI). Several studies showed that endowing robots of human-like interaction skills can not only make them more likeable but also improve their performance. In particular, skill transfer by imitation learning can increase the usability and acceptability of robots by users without computer programming skills. In fact, besides positional information, muscle stiffness of the human arm and contact force with the environment also play important roles in understanding and generating human-like manipulation behaviors for robots, e.g., in physical HRI and teleoperation. To this end, we present a novel robot learning framework based on dynamic movement primitives (DMPs), taking into consideration both the positional and contact force profiles for human-robot skills transferring. Distinguished from the conventional method involving only the motion information, the proposed framework combines two sets of DMPs, which are built to model the motion trajectory and the force variation of the robot manipulator, respectively. Thus, a hybrid force/motion control approach is taken to ensure the accurate tracking and reproduction of the desired positional and force motor skills. Meanwhile, in order to simplify the control system, a momentum-based force observer is applied to estimate the contact force instead of employing force sensors. To deploy the learned motion-force robot manipulation skills to a broader variety of tasks, the generalization of these DMP models in actual situations is also considered. Comparative experiments have been conducted using a Baxter robot to verify the effectiveness of the proposed learning framework on real-world scenarios like cleaning a table.

In this study, the nature of human trust in communication robots was experimentally investigated comparing with trusts in other people and artificial intelligence (AI) systems. The results of the experiment showed that trust in robots is basically similar to that in AI systems in a calculation task where a single solution can be obtained and is partly similar to that in other people in an emotion recognition task where multiple interpretations can be acceptable. This study will contribute to designing a smooth interaction between people and communication robots.

Bidirectional Human-Robot Learning: Imitation and Skill Improvement

Mobile robots are already applied in factories and hospitals, merely to do a distinct task. It is envisioned that robots assist in households soon. Those service robots will have to cope with several situations and tasks and of course with sophisticated human-robot interactions (HRI). Therefore, a robot has not only to consider social rules with respect to proxemics, it must detect in which (interaction) situation it is in and act accordingly. With respect to spatial HRI, we concentrate on the use of non-verbal communication. This chapter stresses the meaning of both, machine movements as signals towards a human and human body language. Considering these aspects will make interaction simpler and smoother. An observational study is presented to acquire a concept of spatial prompting by a robot and by a human. When a person and robot meet in a narrow hallway in order to pass by, they have to make room for each other. But how can a robot make sure that both really want to pass by instead of starting interaction? This especially concerns narrow, non-artificial surroundings. Which social signals are expected by the user and on the other side, can be generated or processed by a robot? The results will show what an appropriate passing behaviour is and how to distinguish between passage situations and others. The results shed light upon the readability of signals in spatial HRI.