The addition of a robot to a team can be difficult if the human teammates do not trust the robot. This can result in underutilization or disuse of the robot, even if the robot has skills or abilities that are necessary to achieve team goals or reduce risk. To help a robot integrate itself with a human team, we present an agent algorithm that allows a robot to estimate its trustworthiness and adapt its behavior accordingly. As behavior adaptation is performed, using case-based reasoning (CBR), information about the adaptation process is stored and used to improve the efficiency of future adaptations.
As the autonomy and capabilities of robotic systems increase, they are expected to play the role of teammates rather than tools and interact with human collaborators in a more realistic manner, creating a more human-like relationship. Given the impact of trust observed in human-robot interaction (HRI), appropriate trust in robotic collaborators is one of the leading factors influencing the performance of human-robot interaction. Team performance can be diminished if people do not trust robots appropriately by disusing or misusing them based on limited experience. Therefore, trust in HRI needs to be calibrated properly, rather than maximized, to let the formation of an appropriate level of trust in human collaborators. For trust calibration in HRI, trust needs to be modeled first. There are many reviews on factors affecting trust in HRI, however, as there are no reviews concentrated on different trust models, in this paper, we review different techniques and methods for trust modeling in HRI. We also present a list of potential directions for further research and some challenges that need to be addressed in future work on human-robot trust modeling.
This paper describes our ongoing research effort to explore how personality types factor into HRI; in particular, the degree of patience a person has when teaching an error-prone robot in a learning from demonstration setting.Our goal is to establish personality metrics that will ultimately allow for the design of algorithms that automatically tune robot behavior to best suit user preferences based on personality.
Teamwork requires mutual trust among team members. Establishing and maintaining trust depends upon alignment of mental models, an aspect of shared awareness. We present a theory of how maintenance of model alignment is integral to fluid changes in relative control authority (i.e., adaptive autonomy) in human-robot teamwork.
Several ongoing research projects in Human autonomous car interactions are addressing the problem of safe co-existence for human and robot drivers on road. Automation in cars can vary across a continuum of levels at which it can replace manual tasks. Social relationships like anthropomorphic behavior of owners towards their cars is also expected to vary according to this spectrum of autonomous decision making capacity. Some researchers have proposed a joint cognitive model of a human-car collaboration that can make the best of the respective strengths of humans and machines. For a successful collaboration, it is important that the members of this human - car team develop, maintain and update each others behavioral models. We consider mutual trust as an integral part of these models. In this paper, we present a review of the quantitative models of trust in automation. We found that only a few models of humans’ trust on automation exist in literature that account for the dynamic nature of trust and may be leveraged in human car interaction. However, these models do not support mutual trust. Our review suggests that there is significant scope for future research in the domain of mutual trust modeling for human car interaction, especially, when considered over the lifetime of the vehicle. Hardware and computational framework (for sensing, data aggregation, processing and modeling) must be developed to support these adaptive models over the operational phase of autonomous vehicles. In order to further research in mutual human - automation trust, we propose a framework for integrating Mutual Trust compu- tation into standard Human - Robot Interaction research platforms. This framework includes User trust and Agent trust, the two fundamental components of Mutual trust. It allows us to harness multi-modal sensor data from the car as well as from the user’s wearable or handheld device. The proposed framework provides access to prior trust aggregate and other cars’ experience data from the Cloud and to feature primitives like gaze, facial expression, etc. from a standard low-cost Human - Robot Interaction platform.