Teaming Up with Robots: An IMOI (Inputs-Mediators-Outputs-Inputs) Framework of Human–Robot Teamwork

Despite the established volume of literature on human–robot interaction, the ways in which humans and robots work together as a team have been relatively understudied. Current approaches to human–robot teamwork do not fully address issues associated with team phenomena that involve multiple humans and robots in the team. In this paper we propose a working framework for human–robot teams, based on an IMOI (inputs-mediators-outputs inputs) framework for teamwork in human teams. The proposed framework describes the developmental process of human–robot teams in which different characteristics of humans and robots produce team outcomes through various mediators within organizational contexts. The framework provides a theoretical guide to better understand how teams working with robots operate and how to improve various team outcomes.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138192/1/You and Robert 2017 (IJRE).pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138192/4/IJRE-2-003 (Current Proof).pdfDescription of You and Robert 2017 (IJRE).pdf : Preprint Versio

Human–Robot Similarity and Willingness to Work with a Robotic Co-worker

Organizations now face a new challenge of encouraging their employees to work alongside robots. In this paper, we address this problem by investigating the impacts of human–robot similarity, trust in a robot, and the risk of physical danger on individuals’ willingness to work with a robot and their willingness to work with a robot over a human co-worker. We report the results from an online experimental study involving 200 participants. Results showed that human–robot similarity promoted trust in a robot, which led to willingness to work with robots and ultimately willingness to work with a robot over a human co-worker. However, the risk of danger moderated not only the positive link between the surface-level similarity and trust in a robot, but also the link between intention to work with the robot and willingness to work with a robot over a human coworker. We discuss several implications for the theory of human–robot interaction and design of robots.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140719/1/HRI 2018_Similarity_0103.pd

High Voltage Insulating Materials-Current State and Prospects

Studies on new solutions in the field of high-voltage insulating materials are presented in this book. Most of these works concern liquid insulation, especially biodegradable ester fluids; however, in a few cases, gaseous and solid insulation are also considered. Both fundamental research as well as research related to industrial applications are described. In addition, experimental techniques aimed at possibly finding new ways of analysing the experimental data are proposed to test dielectrics

GPU Computing for Cognitive Robotics

This thesis presents the first investigation of the impact of GPU computing on cognitive robotics by providing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amounts of computational power, which was until recently provided mostly by standard CPU processors. CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into a highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. This impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This thesis presents several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity enabling the conducting of the novel experiments described herein.European Commission Seventh Framework Programm

Selector’s Guide for Resources in the Social Sciences: An Open Access Publication

Students in the Master of Library and Information Science at Valdosta State University who completed the elective course in Social Sciences Information Services in 2011 produced bibliographies on sub-disciplines of the social sciences. Each bibliography contains representative work in the areas of professional organizations, major serials, online indexes and databases, classic monographs, standard reference works, vetted websites, moving picture documentaries, special collections, and e-government resources. The compilers of this guide offer it as a teaching tool, not a textbook. They invite professors seeking a guide to the providers and formats of information in the social sciences to use the bibliographies therein as a starting point for creating assignments for students of library and information science

Tactile Guidance for Policy Adaptation

Demonstration learning is a powerful and practical technique to develop robot behaviors. Even so, development remains a challenge and possible demonstration limitations, for example correspondence issues between the robot and demonstrator, can degrade policy performance. This work presents an approach for policy improvement through a tactile interface located on the body of the robot. We introduce the Tactile Policy Correction (TPC) algorithm, that employs tactile feedback for the refinement of a demonstrated policy, as well as its reuse for the development of other policies. The TPC algorithm is validated on humanoid robot performing grasp positioning tasks. The performance of the demonstrated policy is found to improve with tactile corrections. Tactile guidance also is shown to enable the development of policies able to successfully execute novel, undemonstrated, tasks. We further show that different modalities, namely teleoperation and tactile control, provide information about allowable variability in the target behavior in different areas of the state space