Influence Of Task-role Mental Models On Human Interpretation Of Robot Motion Behavior

The transition in robotics from tools to teammates has begun. However, the benefit autonomous robots provide will be diminished if human teammates misinterpret robot behaviors. Applying mental model theory as the organizing framework for human understanding of robots, the current empirical study examined the influence of task-role mental models of robots on the interpretation of robot motion behaviors, and the resulting impact on subjective ratings of robots. Observers (N = 120) were exposed to robot behaviors that were either congruent or incongruent with their task-role mental model, by experimental manipulation of preparatory robot task-role information to influence mental models (i.e., security guard, groundskeeper, or no information), the robot\u27s actual task-role behaviors (i.e., security guard or groundskeeper), and the order in which these robot behaviors were presented. The results of the research supported the hypothesis that observers with congruent mental models were significantly more accurate in interpreting the motion behaviors of the robot than observers without a specific mental model. Additionally, an incongruent mental model, under certain circumstances, significantly hindered an observer\u27s interpretation accuracy, resulting in subjective sureness of inaccurate interpretations. The strength of the effects that mental models had on the interpretation and assessment of robot behaviors was thought to have been moderated by the ease with which a particular mental model could reasonably explain the robot\u27s behavior, termed mental model applicability. Finally, positive associations were found between differences in observers\u27 interpretation accuracy and differences in subjective ratings of robot intelligence, safety, and trustworthiness. The current research offers implications for the relationships between mental model components, as well as implications for designing robot behaviors to appear more transparent, or opaque, to humans

Ergonomic Analysis of a Hair Salon

Cosmetology involves a number of diverse tasks that have been implicated in cumulative trauma disorders and in other workplace injuries. This case study presents an analysis of injury risk and prevalence in a salon. Individual, occupational, and organizational factors are considered, and potential areas where risk can be reduced are presented

Leveraging Features Of Human - Technology Teams To Support Mental Models In Future Soldier - Robot Teams

The future vision of military robotics is one in which robots will serve as integrated members of Soldier - robot teams. Robots will possess capabilities that will transition their role from functional tools to working teammates. Because robots and Soldiers will be deployed in environments characterized by uncertainty, complexity, and violence, it is imperative that Soldiers have accurate mental models of what their robotic teammates can do, cannot do, and will likely do. In this paper, we present the conclusions of a review into metaphors for facilitating accurate mental models of robotic teammates. Emphasis was placed on investigating existing human - technology teams (i.e., human teaming with automated systems including autopilot in cockpits, driver assistance systems, and personal assistant applications among others) for features that can support accurate mental models for Soldiers in future Soldier - robot teams

Ergonomic Analysis of a Hair Salon

Cosmetology involves a number of diverse tasks that have been implicated in cumulative trauma disorders and in other workplace injuries. This case study presents an analysis of injury risk and prevalence in a salon. Individual, occupational, and organizational factors are considered, and potential areas where risk can be reduced are presented

Cognitive Models Of Decision Making Processes For Human-Robot Interaction

A fundamental aspect of human-robot interaction is the ability to generate expectations for the decisions of one\u27s teammate(s) in order to coordinate plans of actions. Cognitive models provide a promising approach by allowing both a robot to model a human teammate\u27s decision process as well as by modeling the process through which a human develops expectations regarding its robot partner\u27s actions. We describe a general cognitive model developed using the ACT-R cognitive architecture that can apply to any situation that could be formalized using decision trees expressed in the form of instructions for the model to execute. The model is composed of three general components: instructions on how to perform the task, situational knowledge, and past decision instances. The model is trained using decision instances from a human expert, and its performance is compared to that of the expert. © 2013 Springer-Verlag Berlin Heidelberg

An Investigation Of Human Decision-Making In A Human-Robot Team Task

This paper presents initial insights from an exploratory analysis of human decision making in a human-robot teaming scenario. A cognitive model in the form of a decision tree was developed using local and national police foot pursuit protocols. Participants were asked to read through a series of hypothetical scenarios involving a Soldier and a robot engaging in a foot pursuit of a person of interest. Participants made decisions at each node of the decision tree and then a tactical decision concerning which member of the team should engage in the pursuit. Initial results revealed that individual decision nodes were not associated with participants\u27 choice in who should engage in the pursuit. Trust in robots, however, was significantly associated with the participants\u27 choices

Human Considerations In The Application Of Cognitive Decision Models For Hri

In order for autonomous robots to succeed as useful teammates for humans, it is necessary to examine the lens through which human users view, understand, and predict robotic behavior and abilities. To further study this, we conducted an experiment in which participants viewed video segments of a robot in a task-oriented environment, and were asked to explain what the robot was doing, and would likely do next. Results showed that participants\u27 perceived knowledge of the robot increased with additional exposures over time; however participant responses to open-ended questions about the robot\u27s behavior and functions remained divergent over multiple scenarios. A discussion of the implications of apparent differences in human interpretation and prediction of robotic behavior and functionality is presented. © 2013 Springer-Verlag Berlin Heidelberg

Ergonomic Analysis of a Hair Salon

Cosmetology involves a number of diverse tasks that have been implicated in cumulative trauma disorders and in other workplace injuries. This case study presents an analysis of injury risk and prevalence in a salon. Individual, occupational, and organizational factors are considered, and potential areas where risk can be reduced are presented