A Wearable MYO Gesture Armband Controlling Sphero BB-8 Robot

In this paper we present the development and preliminary validation of a wearable system which is combined with an algorithm interfacing the MYO gesture armband with a Sphero BB-8 robotic device. The MYO armband is a wearable device which measures real-time EMG signals of the end user’s forearm muscles as the user is executing a set of upper limb gestures. These gestures are interpreted and transmitted to a computing hardware via a Bluetooth Low Energy IEEE 802.15.1 wireless protocol. The algorithm analyzes and sorts the data and sends a set of commands to the Sphero robotic device while performing navigation movements. After designing and integrating the software and hardware architecture, we have validated the system with two sets of trials involving a series of commands performed in multiple iterations. The consequent reactions of the robots, due to these commands, were recorded and the performance of the system was analyzed in a confusion matrix to obtain an average accuracy of the system outcome vs. the expected and desired actions. Results show that our integrated system can satisfactorily interface with the system in an intuitive way with an accuracy rating of 85.7 % and 92.9 % for the two tests, respectively

L’implantation de la robotique collaborative et la gestion des ressources humaines dans le secteur manufacturier : soutenir le changement et l’adoption

Ce mémoire de maîtrise explore l’implantation de la robotique collaborative en entreprise sous l’angle des pratiques de gestion et des facteurs humains. La visée initiale de ce projet de recherche visait préalablement à circonscrire l’apport que peut prendre la gestion des ressources humaines (GRH) lors de ce type d’implantation technologique, qui implique une collaboration humain-machine plus accrue qu’auparavant. Initialement, l’objectif était donc d’identifier les pratiques de GRH à mettre en place lors de l’implantation de robots collaboratifs. Cela dit, comme ce projet de recherche présente une démarche exploratoire semi-inductive, l’objectif de recherche a évolué vers plusieurs objectifs. Cette ouverture sur de nouveaux objectifs est subséquente aux résultats obtenus lors de la revue systématique de la littérature et de la collecte de données afin de dresser un portrait plus juste, adapté à l’état des connaissances et au terrain. Les objectifs poursuivis sont les suivants : 1) identifier les pratiques de GRH et d’autres pratiques organisationnelles en matière de gestion du changement facilitant l’implantation et l’adoption des robots collaboratifs 2) identifier les facteurs associés à l’humain, au robot et à l’environnement qui influencent l’implantation des robots collaboratifs, l’adoption et la collaboration entre l’opérateur et le robot

Imitating Human Responses via a Dual-Process Model Approach

Human-autonomous system teaming is becoming more prevalent in the Air Force and in society. Often, the concept of a shared mental model is discussed as a means to enhance collaborative work arrangements between a human and an autonomous system. The idea being that when the models are aligned, the team is more productive due to an increase in trust, predictability, and apparent understanding. This research presents the Dual-Process Model using multivariate normal probability density functions (DPM-MN), which is a cognitive architecture algorithm based on the psychological dual-process theory. The dual-process theory proposes a bipartite decision-making process in people. It labels the intuitive mode as “System 1” and the reflective mode as “System 2”. The current research suggests by leveraging an agent which forms decisions based on a dual-process model, an agent in a human-machine team can maintain a better shared mental model with the user. Evaluation of DPM-MN in a game called Space Navigator shows that DPM-MN presents a successful dual-process theory motivated model

The Influence of Acute Stress on the Perception of Robot Emotional Body Language: Implications for Robot Design in Healthcare and Other High-Risk Domains

University of Minnesota Ph.D. dissertation. July 2017. Major: Human Factors/Ergonomics. Advisors: Kathleen Harder, Wilma Koutstaal. 1 computer file (PDF); viii, 131 pages.In coming years, emotionally expressive social robots will permeate many facets of our lives. Yet, although researchers have explored robot design parameters that may facilitate human-robot interaction, remarkably little attention has been paid to the human perceptual and other psychological factors that may impact human ability to engage with robots. In high-risk settings, such as healthcare—where the use of robots is expected to increase markedly—it is paramount to understand the influence of a patient’s stress level, temperament, and attitudes towards robots as negative interactions could harm a patient’s experience and hinder recovery. Using a novel between-subject paradigm, we investigated how the experimental induction of acute physiological and cognitive stress versus low stress influences perception of normed robot emotional body language as conveyed by a physically-present versus virtual reality generated robot. Following high or low stress induction, participants were asked to rate the valence (negative/unhappy to positive/happy) and level of arousal (calm/relaxed to animated/excited) conveyed by poses in five emotional categories: negative valence-high arousal, negative valence-low arousal, neutral, positive valence-low arousal, positive valence-high arousal. Poses from the categories were randomly intermixed and each pose was presented two or three times. Ratings were then correlated with temperament (as assessed by the Adult Temperament Questionnaire), attitudes towards and experience with robots (a new questionnaire that included measures from the Godspeed Scales and Negative Attitudes about Robots Survey), and chronic stress. The acute stress induction especially influenced the evaluation of high arousal poses – both negative and positive – with both valence and arousal rated lower under high than low stress. Repeated presentation impacted perception of low arousal (negative and positive) and neutral poses, with increases in perceived valence and arousal for later presentations. There were also effects of robot type specifically for positively-valenced emotions, such that these poses were rated as more positive for the physically-present than virtually-instantiated robot. Temperament was found to relate to emotional robot body language. Trait positive affect was associated with higher valence ratings for positive and neutral poses. Trait negative affect was correlated with higher arousal ratings for negative valence-low arousal poses. Subcategories within the robot attitudes questionnaire were correlated with emotional robot poses and temperament. To our knowledge this dissertation is the first exploration of the effects of acute and chronic stress on human perception of robot emotional body language, with implications for robot design, both physical and virtual. Given the largely parallel findings that we observed for the poses presented by the physically-present versus virtually-instantiated robot, it is proposed that the use of virtual reality may provide a viable "sandbox" tool for more efficiently and thoroughly experimenting with possible robot designs, and variants in their emotional expressiveness. Broader psychological, physiological, and other factors that designers should consider as they create robots for high-risk applications are also discussed

Intelligent Team Tutoring: An analysis of communication, cognition, cooperation, and coordination

This thesis describes the evaluation of an Intelligent Team Tutoring System (ITTS) designed to teach team and task skills to improve team and individual performance. Previous work has revealed how team communication, shared situational awareness and mental models, and collective efficacy contribute to the success of a team and how these phenomena are molded by the team members’ interactions. However, less research has explored the impacts of an ITTS on these dimensions of teamwork. The present study was conducted on 37 teams of three who took on one of two roles – spotter (two people) or sniper – in a military-style task. The teams completed three trials in their original roles, then one spotter and the sniper switched roles in the fourth trial. Additionally, individuals either received public or private automated feedback from the ITTS on their performance in the task. Results were mixed. Role experience contributed to the mental model or shared situational awareness of that role as it was defined in training, but not to increased similarity of mental models among teammates. Public feedback positively influenced, although only marginally, the percentage of accurately timed communications and was significantly related to lower overall missed communication actions. Individuals’ performance was also influenced by the frequency of video game play and the amount of team experience, but only for certain actions. Collective efficacy was impacted by an interaction between experience with cooperative gameplay and frequency of video gaming, where individuals with low gaming frequency but high cooperative gameplay experience had significantly lower collective efficacy than low gamers with no or low co-op experience. Lastly, performance errors were related to individuals’ self-reported use of the feedback, in that ignoring the feedback negatively impacted performance, but selectively following the feedback improved performance. Given previous literature on team dynamics and ITSs, these results are largely unexpected but suggest the feedback style had less impact than was predicted. One team dynamic, collective efficacy, was also shown to be impacted by video game team experience in unanticipated ways, indicating that video game experience and team game experience are indirectly influential to team performance. This research enables the designers of future ITTSs to consider the effects of feedback on coordination and communication tasks more carefully and highlights the importance of the design principle of ensuring a transparent mapping between the feedback and the behavioral triggers that led to it

An agent-based reinforcement learning approach to improve human-robot-interaction in manufacturing

This work is aimed at the understanding and application of several emerging technologies as they relate to improving the interactions which occur between robotic operators and their human colleagues across a range of manufacturing processes. These interactions are problematic, as variation in performance of human beings remains one of the largest sources of disturbances within such systems, with potentially significant implications for productivity if it continues unmitigated. The problem remains for the most part unaddressed, despite these interactions becoming increasingly prevalent as the rate of adoption of automation technologies increases. By reconciling multiple areas encompassed by the wider domain of intelligent manufacturing, the presented work identifies a methodology and a set of software tools which leverage the strengths of neural-network-based reinforcement learning to develop intelligent software agents capable of adaptable behaviour in response to observed environmental changes. The methodology further focuses on developing representative simulation models for these interactions following a pattern of generalisation, to effectively represent both human and robotic elements, and facilitate implementation. By learning through their interaction with the simulated manufacturing environment, these agents can determine an appropriate policy, by which to autonomously adjust their operating parameters, as a response to changes in their human colleagues. This adaptability is demonstrated to enable the intelligent agents to determine an action policy which results in less observed idle time, along with improved leanness and overall productivity, over multiple scenarios. The findings of the work suggest that software agents that make use of a reinforcement based learning approach are well suited to the task of enabling robotic adaptability in such a way, and the developed methodology provides a platform for further development and exploration, along with numerous insights into the effective development of these agents