Paul Robinette

Shailesh Nirgudkar, Michael DeFilippo, Michael Sacarny et al.

Recent advances in deep learning technology have triggered radical progress in the autonomy of ground vehicles. Marine coastal Autonomous Surface Vehicles (ASVs) that are regularly used for surveillance, monitoring and other routine tasks can benefit from this autonomy. Long haul deep sea transportation activities are additional opportunities. These two use cases present very different terrains -- the first being coastal waters -- with many obstacles, structures and human presence while the latter is mostly devoid of such obstacles. Variations in environmental conditions are common to both terrains. Robust labeled datasets mapping such terrains are crucial in improving the situational awareness that can drive autonomy. However, there are only limited such maritime datasets available and these primarily consist of optical images. Although, Long Wave Infrared (LWIR) is a strong complement to the optical spectrum that helps in extreme light conditions, a labeled public dataset with LWIR images does not currently exist. In this paper, we fill this gap by presenting a labeled dataset of over 2,900 LWIR segmented images captured in coastal maritime environment under diverse conditions. The images are labeled using instance segmentation and classified in seven categories -- sky, water, obstacle, living obstacle, bridge, self and background. We also evaluate this dataset across three deep learning architectures (UNet, PSPNet, DeepLabv3) and provide detailed analysis of its efficacy. While the dataset focuses on the coastal terrain it can equally help deep sea use cases. Such terrain would have less traffic, and the classifier trained on cluttered environment would be able to handle sparse scenes effectively. We share this dataset with the research community with the hope that it spurs new scene understanding capabilities in the maritime environment.

Maitry Ronakbhai Trivedi, Zahra Rezaei Khavas, Paul Robinette

Human-Robot Interaction, in which a robot with some level of autonomy interacts with a human to achieve a specific goal has seen much recent progress. With the introduction of autonomous robots and the possibility of widespread use of those in near future, it is critical that humans understand the robot's intention while interacting with them as this will foster the development of human-robot trust. The new conceptualization of trust which had been introduced by researchers in recent years considers trust in Human-Robot Interaction to be a multidimensional nature. Two main aspects which are attributed to trust are performance trust and moral trust. We aim to design an experiment to investigate the consequences of performance-trust violation and moral-trust violation in a search and rescue scenario. We want to see if two similar robot failures, one caused by a performance-trust violation and the other by a moral-trust violation have distinct effects on human trust. In addition to this, we plan to develop an interface that allows us to investigate whether altering the interface's modality from grid-world scenario (2D environment) to realistic simulation (3D environment) affects human perception of the task and the effects of the robot's failure on human trust.

Russell Perkins, Zahra Rezaei Khavas, Paul Robinette

Recent advances in the areas of human-robot interaction (HRI) and robot autonomy are changing the world. Today robots are used in a variety of applications. People and robots work together in human autonomous teams (HATs) to accomplish tasks that, separately, cannot be easily accomplished. Trust between robots and humans in HATs is vital to task completion and effective team cohesion. For optimal performance and safety of human operators in HRI, human trust should be adjusted to the actual performance and reliability of the robotic system. The cost of poor trust calibration in HRI, is at a minimum, low performance, and at higher levels it causes human injury or critical task failures. While the role of trust calibration is vital to team cohesion it is also important for a robot to be able to assess whether or not a human is exhibiting signs of mistrust due to some other factor such as anger, distraction or frustration. In these situations the robot chooses not to calibrate trust, instead the robot chooses to respect trust. The decision to respect trust is determined by the robots knowledge of whether or not a human should trust the robot based on its actions(successes and failures) and its feedback to the human. We show that the feedback in the form of trust calibration cues(TCCs) can effectively change the trust level in humans. This information is potentially useful in aiding a robot it its decision to respect trust.

Zahra Rezaei Khavas, Russell Perkins, S. Reza Ahmadzadeh et al.

In recent years a modern conceptualization of trust in human-robot interaction (HRI) was introduced by Ullman et al.\cite{ullman2018does}. This new conceptualization of trust suggested that trust between humans and robots is multidimensional, incorporating both performance aspects (i.e., similar to the trust in human-automation interaction) and moral aspects (i.e., similar to the trust in human-human interaction). But how does a robot violating each of these different aspects of trust affect human trust in a robot? How does trust in robots change when a robot commits a moral-trust violation compared to a performance-trust violation? And whether physiological signals have the potential to be used for assessing gain/loss of each of these two trust aspects in a human. We aim to design an experiment to study the effects of performance-trust violation and moral-trust violation separately in a search and rescue task. We want to see whether two failures of a robot with equal magnitudes would affect human trust differently if one failure is due to a performance-trust violation and the other is a moral-trust violation.

Zahra Rezaei Khavas, Reza Ahmadzadeh, Paul Robinette

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.