A Survey of User Interfaces for Robot Teleoperation

Robots are used today to accomplish many tasks in society, be it in industry, at home, or as helping tools on tragic incidents. The human-robot systems currently developed span a broad variety of applications and are typically very different from one another. The interaction techniques designed for each system are also very different, although some effort has been directed in defining common properties and strategies for guiding human-robot interaction (HRI) development. This work aims to present the state-of-the-art in teleoperation interaction techniques between robots and their users. By presenting potentially useful design models and motivating discussions on topics to which the research community has been paying little attention lately, we also suggest solutions to some of the design and operational problems being faced in this area

Autonomous, Context-Sensitive, Task Management Systems and Decision Support Tools I: Human-Autonomy Teaming Fundamentals and State of the Art

Recent advances in artificial intelligence, machine learning, data mining and extraction, and especially in sensor technology have resulted in the availability of a vast amount of digital data and information and the development of advanced automated reasoners. This creates the opportunity for the development of a robust dynamic task manager and decision support tool that is context sensitive and integrates information from a wide array of on-board and off aircraft sourcesa tool that monitors systems and the overall flight situation, anticipates information needs, prioritizes tasks appropriately, keeps pilots well informed, and is nimble and able to adapt to changing circumstances. This is the first of two companion reports exploring issues associated with autonomous, context-sensitive, task management and decision support tools. In the first report, we explore fundamental issues associated with the development of an integrated, dynamic, flight information and automation management system. We discuss human factors issues pertaining to information automation and review the current state of the art of pilot information management and decision support tools. We also explore how effective human-human team behavior and expectations could be extended to teams involving humans and automation or autonomous systems

Wearables at work:preferences from an employee’s perspective

This exploratory study aims to obtain a first impression of the wishes and needs of employees on the use of wearables at work for health promotion. 76 employ-ees with a mean age of 40 years old (SD ±11.7) filled in a survey after trying out a wearable. Most employees see the potential of using wearable devices for workplace health promotion. However, according to employees, some negative aspects should be overcome before wearables can effectively contribute to health promotion. The most mentioned negative aspects were poor visualization and un-pleasantness of wearing. Specifically for the workplace, employees were con-cerned about the privacy of data collection

Supporting working time interruption management through persuasive design

Indiana University-Purdue University Indianapolis (IUPUI)Knowledge workers often suffer productivity loss because of unsuccessful interruption handling, which can lead to even more detrimental behaviors like "cyber-slacking" and procrastination. Many of the interruption management techniques proposed in the research literature focus on minimizing interruption occurrences. However, given the inevitability of internal and external interruptions in everyday life, it may be more practical to help people regulate how they respond to interruptions using persuasive technologies. The aim of this dissertation is to explore and evaluate the design of persuasive computer agents that encourage information workers to resume interrupted work. Based on a systematic review of interruptions in the workplace, theories of self-regulation, and theories guiding the design of persuasive technologies, this dissertation describes the creation of a prototype research platform, WiredIn. WiredIn enables researchers to explore a variety of interruption resumption support strategies on desktop computers. Two empirical studies that investigate the efficacy, attributes, and consequences of applying the paradigms embodied in WiredIn in controlled and real-life working environments are presented here. Both studies validate the effect of persuasive interventions on improving interruption management behaviors; the second study also provides design suggestions that can inform future work in supporting interruption management and multitasking

Acquisition and distribution of synergistic reactive control skills

Learning from demonstration is an afficient way to attain a new skill. In the context of autonomous robots, using a demonstration to teach a robot accelerates the robot learning process significantly. It helps to identify feasible solutions as starting points for future exploration or to avoid actions that lead to failure. But the acquisition of pertinent observationa is predicated on first segmenting the data into meaningful sequences. These segments form the basis for learning models capable of recognising future actions and reconstructing the motion to control a robot. Furthermore, learning algorithms for generative models are generally not tuned to produce stable trajectories and suffer from parameter redundancy for high degree of freedom robots This thesis addresses these issues by firstly investigating algorithms, based on dynamic programming and mixture models, for segmentation sensitivity and recognition accuracy on human motion capture data sets of repetitive and categorical motion classes. A stability analysis of the non-linear dynamical systems derived from the resultant mixture model representations aims to ensure that any trajectories converge to the intended target motion as observed in the demonstrations. Finally, these concepts are extended to humanoid robots by deploying a factor analyser for each mixture model component and coordinating the structure into a low dimensional representation of the demonstrated trajectories. This representation can be constructed as a correspondence map is learned between the demonstrator and robot for joint space actions. Applying these algorithms for demonstrating movement skills to robot is a further step towards autonomous incremental robot learning

Some consequences of vulnerability in consumers' life

Under the label of consumer vulnerability, researchers have included a variety of studies that focus on consumers who face challenging situations in the marketplace because of consumers’ individual characteristics (e.g., age, income), external conditions (e.g., stereotypes, repression), or individual states (e.g., grief, mood). In this dissertation, we propose that consumers are vulnerable not only because they belong to a specific socioeconomic group, but also because they lack a combination of resources-control that makes them susceptible to marketplace harm. Moreover, we investigate psychological mechanisms behind vulnerability, and potential coping strategies consumers might use when dealing with vulnerability. In particular, we focus on three types of vulnerabilities: (1) lack of financial resources, (2) experience of physical pain, and (3) feeling observed and replaced by artificial intelligence (AI). In Chapter 1, we theoretically propose, and empirically test the negative effect of feeling financially constrained on participation in access-based services (ABS henceforth) across five studies. Based on previous findings, we predict that feeling financially constrained will reduce consumers’ willingness to use ABS for three main reasons. First, financially constrained consumers prefer lasting products over experiences. Second, financially constrained consumers tend to avoid behaviors that reinforce negative feelings about their financial situation. Third, the fear of being judged negatively by others for using “cheap solutions” as ABS would lead financially constrained consumers to favor the traditional option (buying) over the more innovative way of consumption (ABS). In Chapter 2, we build on previous research showing that lack of financial resources can affect individuals’ cognitive abilities. We propose that social capital can alleviate the financial concern of the poor and restore their cognitive capacity. Through social capital, individuals can experience different benefits: they can gain direct access to economic resources (i.e., subsidized loans, investment tips, protected markets); they can increase their cultural capital through contacts with experts or individuals of refinement (i.e., embodied cultural capital); or, alternatively, they can affiliate with institutions that confer valued credentials (i.e., institutionalized cultural capital). We believe that social capital is a resource the poor may use to compensate for the lack of financial resources. Three lab studies provide partial support for our hypothesis. In Chapter 3, we study how the presence of AI surveillance technologies affects citizens’ willingness to help in a public context. In particular, we build on recent findings to show that the presence of AI technologies can either increase or decrease citizens’ willingness to help. When people feel observed, people tend to act according to social norms and help more. However, according to the transhumanist narrative, when AI becomes more independent, people are more likely to delegate tasks to AI and help less others. In two studies, we show that citizens feel less responsible to intervene and help less when they perceive AI as able to help instead of them. Citizens tend to help more when they perceive AI as having lower agency. Finally, in Chapter 4, we examine the influence of physical pain on consumers’ willingness to conform to others. When people are in pain, they often feel threatened and look at others to feel reassured. Individuals who are in pain often experience lack of control and helplessness. Groups can serve as a resource to empower people who lack a sense of personal agency and control. Specifically, a threat to personal control increases people’s readiness to act as group members. Therefore, we expect that higher physical pain will lead to higher willingness to conform. To test our hypothesis, we are conducting a lab experiment with a heating circulator machine used to manipulate pain, and one online study

Toward Data-Driven Digital Therapeutics Analytics: Literature Review and Research Directions

With the advent of Digital Therapeutics (DTx), the development of software as a medical device (SaMD) for mobile and wearable devices has gained significant attention in recent years. Existing DTx evaluations, such as randomized clinical trials, mostly focus on verifying the effectiveness of DTx products. To acquire a deeper understanding of DTx engagement and behavioral adherence, beyond efficacy, a large amount of contextual and interaction data from mobile and wearable devices during field deployment would be required for analysis. In this work, the overall flow of the data-driven DTx analytics is reviewed to help researchers and practitioners to explore DTx datasets, to investigate contextual patterns associated with DTx usage, and to establish the (causal) relationship of DTx engagement and behavioral adherence. This review of the key components of data-driven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets, which helps to iteratively improve the receptivity of existing DTx.Comment: This paper has been accepted by the IEEE/CAA Journal of Automatica Sinic