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.
Perceived characteristics of a robot including competence, responsibility, and credibility (and combinations of these), have been proposed to provide means for evaluating the level of trust in a machine @cite_24 @cite_11 . Still, a clear stance on what exactly is being measured is necessary @cite_35 (e.g. trust in terms of competence as in @cite_24 , or trustworthiness' directly, as in @cite_9 ). Measuring trust in human-robot interactions is challenging. A momentary state of trust can be captured in an experiment, but trust is earned over a longer term @cite_11 @cite_30 @cite_9 . Accumulation of small errors might affect trust more than a single large error, and a poor initial performance by a robot could leave a stronger negative impact @cite_24 . Also, trust is not only a situational construct (i.e. depending on the situation), but it is also dispositional (i.e. depends on the individual and its characteristics) @cite_30 . Additionally, the study of human-robot interactions through experiments has many practical and ethical issues and associated limitations, as participants should not be exposed to dangerous situations, nor should they be deceived @cite_34 .
@cite_30 @cite_35 @cite_9 @cite_24 @cite_34 @cite_11
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Prior work in human trust of autonomous robots suggests the timing of reliability drops impact trust and control allocation strategies. However, trust is traditionally measured post-run, thereby masking the real-time changes in trust, reducing sensitivity to factors like inertia, and subjecting the measure to biases like the primacy-recency effect. Likewise, little is known on how feedback of robot confidence interacts in real-time with trust and control allocation strategies. An experiment to examine these issues showed trust loss due to early reliability drops is masked in traditional post-run measures, trust demonstrates inertia, and feedback alters allocation strategies independent of trust. The implications of specific findings on development of trust models and robot design are also discussed. We describe a model of “trust” in human-robot systems that is inferred from their interactions, and inspired by similar concepts relating to trust among humans. This computable quantity allows a robot to estimate the extent to which its performance is consistent with a human's expectations, with respect to task demands. Our trust model drives an adaptive mechanism that dynamically adjusts the robot's autonomous behaviors, in order to improve the efficiency of the collaborative team. We illustrate this trust-driven methodology through an interactive visual robot navigation system. This system is evaluated through controlled user experiments and a field demonstration using an aerial robot. The ability to perceive humans is an essential requirement for safe and efficient human-robot interaction. In real-world applications, the need for a robot to interact in real time with multiple humans in a dynamic, 3-D environment presents a significant challenge. The recent availability of commercial color-depth cameras allow for the creation of a system that makes use of the depth dimension, thus enabling a robot to observe its environment and perceive in the 3-D space. Here we present a system for 3-D multiple human perception in real time from a moving robot equipped with a color-depth camera and a consumer-grade computer. Our approach reduces computation time to achieve real-time performance through a unique combination of new ideas and established techniques. We remove the ground and ceiling planes from the 3-D point cloud input to separate candidate point clusters. We introduce the novel information concept, depth of interest, which we use to identify candidates for detection, and that avoids the computationally expensive scanning-window methods of other approaches. We utilize a cascade of detectors to distinguish humans from objects, in which we make intelligent reuse of intermediary features in successive detectors to improve computation. Because of the high computational cost of some methods, we represent our candidate tracking algorithm with a decision directed acyclic graph, which allows us to use the most computationally intense techniques only where necessary. We detail the successful implementation of our novel approach on a mobile robot and examine its performance in scenarios with real-world challenges, including occlusion, robot motion, nonupright humans, humans leaving and reentering the field of view (i.e., the reidentification challenge), human-object and human-human interaction. We conclude with the observation that the incorporation of the depth information, together with the use of modern techniques in new ways, we are able to create an accurate system for real-time 3-D perception of humans by a mobile robot. Recognition of intentions is a subconscious cognitive process vital to human communication. This skill enables anticipation and increases the quality of interactions between humans. Within the context of engagement, non-verbal signals are used to communicate the intention of starting the interaction with a partner. In this paper, we investigated methods to detect these signals in order to allow a robot to know when it is about to be addressed. Originality of our approach resides in taking inspiration from social and cognitive sciences to perform our perception task. We investigate meaningful features, i.e. human readable features, and elicit which of these are important for recognizing someone's intention of starting an interaction. Classically, spatial information like the human position and speed, the human-robot distance are used to detect the engagement. Our approach integrates multimodal features gathered using a companion robot equipped with a Kinect. The evaluation on our corpus collected in spontaneous conditions highlights its robustness and validates the use of such a technique in a real environment. Experimental validation shows that multimodal features set gives better precision and recall than using only spatial and speed features. We also demonstrate that 7 selected features are sufficient to provide a good starting engagement detection score. In our last investigation, we show that among our full 99 features set, the space reduction is not a solved task. This result opens new researches perspectives on multimodal engagement detection. Multimodal approach for starting engagement detection using non-explicit cues.Results show that our approach performs better than spatial one in all conditions.MRMR strategy reduces the features space to 7 features without a performance loss.Validation of Schegloff (sociologist) meaningful features for engagement detection.A robot centered labeled corpus of 4 hours in a home-like environment.