Creative Robotics Lab

This paper reports the results from an experiment examining people’s perception and trust when interacting with an android robot. Also, they engaged in an economic trust game with the robot. We used proxemics, the physical distance to the robot, and questionnaires to measure the participants` character and their perception of the robot. We found influences of the subject’s character onto the amount sent in the trust game and distance changes over the 3 interaction tasks. The perception of the robot changed after the interaction trials towards less anthropomorph and less intelligent, but safer. This study would enable future researches to compare different robot types, personality traits and cross-cultural effects.

This paper reports the results from an experiment examining people's perception and trust when interacting with an android robot. Also, they engaged in an economic trust game with the robot. We used proxemics, the physical distance to the robot, and questionnaires to measure the participants` character and their perception of the robot. We found influences of the subject's character onto the amount sent in the trust game and distance changes over the 3 interaction tasks. The perception of the robot changed after the interaction trials towards less anthropomorph and less intelligent, but safer. This study would enable future researches to compare different robot types, personality traits and cross-cultural effects.

Cognitive architectures can account for highly complex tasks. One of the greatest challenges is understanding and modeling human driving behavior. This paper describes an integrated cognitive model of human attention during the performance of car driving. In this task, the attention process can be divided into at least three basic components: the control process, the monitoring process, and finally, the decision making process. Of these basic tasks, the first has the highest priority. All three phases are implemented in a cognitive model in the cognitive Architecture ACT-R 6.0. The model is able to keep a traffic lane, overtake another vehicle by lane change, identifies traffic signs and different situations emerging at crossroads.

This study examines the assumptions, perceptions, and attitudes towards contemporary robots in different cultures. As culture affects the way technology is perceived, it is essential to explore the psychological reactions through different cultures towards different designs of robots. Only a few studies have examined attitudes towards robots or dealt with the assumptions in a specific culture. The present study determines the influence of crucial factors like culture, prior exposure to robots through the media, and personal experiences of the emotions towards robots and examines the cultural differences between Europe and Japan.

Driving a car is obviously a complex task and the construction of an ACT-R model of human attention while performing this task is similarly complex along multiple dimensions and presents a challenge to architecture and modeler. This work is a first attempt to develop an integrated driver model of attention in the cognitive architecture ACT-R. The model is able to keep a traffic lane, identifies traffic signs and crossroads in a sparse, simulated environment.

People seem to miscalibrate their expectations and interactions with a robot. When it comes to robot design, the anthropomorphism level of the robot form (appearance) has become an increasingly important variable to consider. It is argued here that people base their expectations and perceptions of a robot on its form and attribute functions which do not necessarily mirror the true functions of the robot. The term Form Function Attribution Bias (FFAB) refers to the cognitive bias which occurs when people are prone to perceptual errors, leading to a biased interpretation of a robot's functionality. We argue that rather than objectively perceiving the robot's functionalities, people take a cognitive shortcut using the information available to them through visual perception. FFAB intends to outline the implications the design of a robot has on the human predisposition to interact socially with robots. In this theoretical review, we examined the results of several studies suggesting a FFAB. We outline future directions of experimental paradigms and robot design implications.

Because one of the largest influences on trust in automation is the familiarity with the system, we sought to examine the effects of familiarity on driver interventions while using the autoparking feature of a Tesla Model X. Participants were either told or shown how the autoparking feature worked. Results showed a significantly higher initial driver intervention rate when the participants were only told how to employ the autoparking feature, than when shown. However, the intervention rate quickly leveled off, and differences between conditions disappeared. The number of interventions and the distances from the parking anchoring point (a trashcan) were used to create a new measure of distrust in autonomy. Eyetracking measures revealed that participants disengaged from monitoring the center display as the experiment progressed, which could be a further indication of a lowering of distrust in the system. Combined, these results have important implications for development and design of...

The COVID-19 pandemic will have a profound and long-lasting impact on the entire scientific endeavor. Scientists already are adapting research programs to adapt to changes in what is prioritized—and what is possible; educators are changing the way that the next generation of researchers are trained, and flagship conferences in many fields are being cancelled, postponed, and fundamentally transformed. These broad-reaching changes are particularly impactful to human-oriented domains such as human-robot interaction (HRI). Because in-person human-subject experiments can take a year or more to conduct, the research we will see published in the field in the immediate future may appear to be “business as usual,” with accounts of laboratory studies with large numbers of in-person participants. The research currently being performed, however, is of course a different story entirely. Studies that were under way when the current crisis began will be truncated, resulting either in work that can...

Prior studies have shown research projects completed as part of undergraduate curricula are not only becoming more commonplace, but they also provide substantial benefit to student learning. Given this evidence, the Department of Behavioral Sciences and Leadership (DFBL) at the United States Air Force Academy has recently required capstone research for every cadet in the behavioral sciences major. The purpose of this paper is to describe how the human factors program has structured the capstone experience within the Warfighter Effectiveness Research Center (WERC) in DFBL. Even though some research experience has been a longstanding requirement in the human factors program for many years, the capstone sequence has led to a shift in what undergraduates are accomplishing in and out of the classroom. In addition to examples of current projects, we provide several recommendations for academic institutions to implement capstone projects.

Current U.S. Department of Defense (DoD) and Air Force strategic guidance anticipates fully autonomous robotic teammates operating alongside airman on the battlefield of the future (e.g., Endsley, 2015). It is anticipated that these robots will possess highly developed computational capabilities, allowing them to act in sophisticated roles in mixed human-machine teams. Furthermore, it is expected that these machines will have some degree of autonomous decision-making capabilities, and that humans will consult with and rely on the advice and guidance of their robotic teammates. To understand how operators will work alongside these sophisticated robot teammates, it is necessary to understand the factors that influence human-robot interactions (HRI). Two dimensions that have been found to influence HRI are embodiment and anthropomorphism. Humans report higher engagement, helpfulness, and persuasiveness (Bainbridge et al.

The anticipated social capabilities of robots may allow them to serve in authority roles as part of human-machine teams. To date, it is unclear if, and to what extent, human team members will comply with requests from their robotic teammates, and how such compliance compares to requests from human teammates. This research examined how the human-likeness and physical embodiment of a robot affect compliance to a robot's request to perseverate utilizing a novel task paradigm. Across a set of two studies, participants performed a visual search task while receiving ambiguous performance feedback. Compliance was evaluated when the participant requested to stop the task and the coach urged the participant to keep practicing multiple times. In the first study, the coach was either physically co-located with the participant or located remotely via a live-video. Coach type varied in human-likeness and included either a real human (confederate), a Nao robot, or a modified Roomba robot. The...

Socially intelligent artificial agents and robots are anticipated to become ubiquitous in home, work, and military environments. With the addition of such agents to human teams it is crucial to evaluate their role in the planning, decision making, and conflict mediation processes. We conducted a study to evaluate the utility of a virtual agent that provided mission planning support in a three-person human team during a military strategic mission planning scenario. The team consisted of a human team lead who made the final decisions and three supporting roles, two humans and the artificial agent. The mission outcome was experimentally designed to fail and introduced a conflict between the human team members and the leader. This conflict was mediated by the artificial agent during the debriefing process through discuss or debate and open communication strategies of conflict resolution [1]. Our results showed that our teams experienced conflict. The teams also responded socially to the virtual agent, although they did not find the agent beneficial to the mediation process. Finally, teams collaborated well together and perceived task proficiency increased for team leaders. Socially intelligent agents show potential for conflict mediation, but need careful design and implementation to improve team processes and collaboration.

While social robots are designed to engage in socially interactive tasks, they may not always establish the intended social connection. We examined how people's experiences of succeeding in completing these interactive tasks influence attitudes toward social robots. People developed more positive attitudes toward social robots when they completed more tasks successfully. These findings highlight potential constraints of complex interactive tasks increasingly implemented in commercially available social robots. A trade-off may exist between early task success and the sustained training of complex social robots by their human social partners.

This article provides an overview on how modern neuroscience evaluations link to robot empathy. It evaluates the brain correlates of empathy and caregiving, and how they may be related to the higher functions with an emphasis on women. We discuss that the understanding of the brain correlates can inform the development of social robots with enhanced empathy and caregiving abilities. We propose that the availability of these robots will benefit many aspects of the society including transition to parenthood and parenting, in which women are deeply involved in real life and scientific research. We conclude with some of the barriers for women in the field and how robotics and robot empathy research benefits from a broad representation of researchers.

Functional Near-Infrared Spectroscopy (fNIRS) is an innovative and promising neuroimaging modality for studying brain activity in real-world environments. While fNIRS has seen rapid advancements in hardware, software, and research applications since its emergence nearly 30 years ago, limitations still exist regarding all three areas, where existing practices contribute to greater bias within the neuroscience research community. We spotlight fNIRS through the lens of different end-application users, including the unique perspective of a fNIRS manufacturer, and report the challenges of using this technology across several research disciplines and populations. Through the review of different research domains where fNIRS is utilized, we identify and address the presence of bias, specifically due to the restraints of current fNIRS technology, limited diversity among sample populations, and the societal prejudice that infiltrates today's research. Finally, we provide resources for min...

The creation of information transparency solutions to enable humans to understand robot perception is a challenging requirement for autonomous and artificially intelligent robots to impact a multitude of domains. By taking advantage of comprehensive and high-volume data from robot teammates’ advanced perception and reasoning capabilities, humans will be able to make better decisions, with significant impacts from safety to functionality. We present a solution to this challenge by coupling augmented reality (AR) with an intelligent mobile robot that is autonomously detecting novel changes in an environment. We show that the human teammate can understand and make decisions based on information shared via AR by the robot. Sharing of robot-perceived information is enabled by the robot’s online calculation of the human’s relative position, making the system robust to environments without external instrumentation such as GPS. Our robotic system performs change detection by comparing curre...

In this paper, we examine the risks posed by roboticists' collaboration with law enforcement agencies in the U.S. Using Trust frameworks from AI Ethics, we argue that collaborations with law enforcement present not only risks of technology misuse, but also risks of legitimizing bad actors, and of exacerbating our field's challenges of representation. We discuss evidence of bad dispositions justifying these risks, grounded in the behavior, origins, and incentivization of American policing, and suggest courses of action for American roboticists seeking to pursue research projects that currently require collaboration with law enforcement agencies, closing with a call for abolitionist robotics. CCS CONCEPTS • Computer systems organization → Robotics; • Applied computing → Law, social and behavioral sciences.

Optimising compilers present their authors with an intractable design space. A substantial body of work has used heuristic search techniques to search this space for the purposes of adapting optimisers to their environment. To date, most of this work has focused on sequencing, tuning and guiding the actions of atomic hand-written optimisation phases. In this paper we explore the adaption of optimisers at a deeper level by demonstrating that it is feasible to automatically build a non-trivial optimisation phase, for a simple functional language, using grammatical evolution. We show that the individuals evolved compare well in performance to a hand-written optimisation phase on a range of benchmarks. We conclude with proposals of how this work and its applications can be extended.

A novel contextual logic is presented that combines features of both multi-context systems and logics of context. Broadly, contextual logics are those with a formal notion of context — knowledge that is true only under specific assumptions. Multi-context systems use discrete logistic systems as individual contexts, related by meta-level rules, whereas logics of context partition a single knowledge base into contexts, related using object-level rules. The contextual logic presented here is strongly-local, in that knowledge and inference is discrete for individual contexts, but which are nevertheless part of a single logistic system that relates contexts at the object-level, so combining advantages of both. A deduc- tive system of contextual inference and a possible- worlds based semantics is given, with formal results including soundness and completeness, and a number of properties are examined.