Multi-Operator Gesture Control of Robotic Swarms Using Wearable Devices

The theory and design of effective interfaces for human interaction with multi-robot systems has recently gained significant interest. Robotic swarms are multi-robot systems where local interactions between robots and neighbors within their spatial neighborhood generate emergent collective behaviors. Most prior work has studied interfaces for human interaction with remote swarms, but swarms also have great potential in applications working alongside humans, motivating the need for interfaces for local interaction. Given the collective nature of swarms, human interaction may occur at many levels of abstraction ranging from swarm behavior selection to teleoperation. Wearable gesture control is an intuitive interaction modality that can meet this requirement while keeping operator hands usually unencumbered. In this paper, we present an interaction method using a gesture-based wearable device with a limited number of gestures for robust control of a complex system: a robotic swarm. Experiments conducted with a real robot swarm compare performance in single and two-operator conditions illustrating the effectiveness of the method. Results show human operators using our interaction method are able to successfully complete the task in all trials, illustrating the effectiveness of the method, with better performance in the two-operator condition, indicating separation of function is beneficial for our method. The primary contribution of our work is the development and demonstration of interaction methods that allow robust control of a difficult to understand multi robot system using only the noisy inputs typical of smartphones and other on-body sensor driven devices

An Adversarial Approach to Private Flocking in Mobile Robot Teams

Privacy is an important facet of defence against adversaries. In this letter, we introduce the problem of private flocking . We consider a team of mobile robots flocking in the presence of an adversary, who is able to observe all robots’ trajectories, and who is interested in identifying the leader. We present a method that generates private flocking controllers that hide the identity of the leader robot. Our approach towards privacy leverages a data-driven adversarial co-optimization scheme. We design a mechanism that optimizes flocking control parameters, such that leader inference is hindered. As the flocking performance improves, we successively train an adversarial discriminator that tries to infer the identity of the leader robot. To evaluate the performance of our co-optimization scheme, we investigate different classes of reference trajectories. Although it is reasonable to assume that there is an inherent trade-off between flocking performance and privacy, our results demonstrate that we are able to achieve high flocking performance and simultaneously reduce the risk of revealing the leader

Human Interaction Through an Optimal Sequencer to Control Robotic Swarms

The interaction between swarm robots and human operators is significantly different from the traditional humanrobot interaction due to unique characteristics of the system, such as high cognitive complexity and difficulties in state estimation. In this paper, we concentrate on a method for conveying input from the operator to the swarm. Previous research has shown that control through switching between behaviors offers the greatest flexibility but is particularly difficult for human operators. A recently developed method for finding optimal sequences for composing behaviors offers a potential tool for aiding human operators controlling swarms through behavior switching. This paper compares participants performing a navigation task with and without the availability of an optimal sequencing aid. Results show that the task of preplanning a sequence of behaviors and durations is more difficult for participants than switching between executing behaviors to navigate. Users who used the aid frequently created shorter paths than infrequent users and the control group. In the trials that the aid was used, participants tended to generate more complicated sequences and achieve the first attempt more rapidly, than trials in which the aid was not used

Human vs. Deep Neural Network Performance at a Leader Identification Task

Control of robotic swarms through control over a leader(s) has become the dominant approach to supervisory control over these largely autonomous systems. Resilience in the face of attrition is one of the primary advantages attributed to swarms yet the presence of leader(s) makes them vulnerable to decapitation. Algorithms which allow a swarm to hide its leader are a promising solution. We present a novel approach in which neural networks, NNs, trained in a graph neural network, GNN, replace conventional controllers making them more amenable to training. Swarms and an adversary intent of finding the leader were trained and tested in 4 phases: 1-swarm to follow leader, 2-adversary to recognize leader, 3-swarm to hide leader from adversary, and 4-swarm and adversary compete to hide and recognize the leader. While the NN adversary was more successful in identifying leaders without deception, humans did better in conditions in which the swarm was trained to hide its leader from the NN adversary. The study illustrates difficulties likely to emerge in arms races between machine learners and the potential role humans may play in moderating them

Catalyzing Change in Higher Education: Social Capital and Network Leadership in the Competency-Based Education Network

Collaborative inter-organizational networks can be effective at catalyzing and supporting the generation and diffusion of new models and practices. With shared purpose, structure, and resources, network organizations can facilitate knowledge exchange and the growth of inter-organizational relationships. In this study, I sought to better understand how network organizations influence social capital and the spread of innovative practices. Of particular interest were the roles of national network and sub-national network organizations (sub-networks), and the interactive learning processes of network newcomers. I focused on the diverse array of colleges and universities involved in the Competency-Based Education Network (C-BEN), and their efforts to transform higher education practice and policy. Specific research questions were tackled to understand: (a) the dimensions of key collaborative relationships (KCRs) and their associations to outcomes; (b) the competency-based education (CBE) ecosystem’s network structure, important clusters of network activity, and key individual and organizational actors; (c) the association between KCRs and the implementation of similar CBE practices; (d) the organizational and individual factors associated with the formation of inter-organizational KCRs; and, (e) the experiences of HEIs new-to-CBE as they learn about CBE, and then design and implement new programs. A mixed methods sequential explanatory research design was employed using social network analysis and qualitative case methods. Study data was drawn from multiple sources, to include the study CBE Social Network Survey (CBESNS), a confidential American Institutes for Research survey, and from 36 semi-structured interviews. Results confirmed that strong ties and trust were important to tacit knowledge transfer and organizational innovation, and a strong correlation was found between inter-organizational collaborative work and trust. Immersive problem-solving programs were found effective for growing trust and strong relations among diverse stakeholders, along with advancing innovations in policy and practice. Lastly, a bifurcated learning process was seen for newcomers based on their potential affiliation to sub-network organizations, which connected them with impactful proximal influencers, among other benefits. Contributions to the literature are made with findings that have both theoretical and practical implications. They also anchor a research agenda for understanding how transformation can be enacted in complex systems and sectors through networks

The substitution of labor: From technological feasibility to other factors influencing the potential of job automation

Artificial intelligence, machine learning (a subcategory of AI), and robotics are three technologies that perform an increasingly wider variety of routine and even non-routine job tasks. This chapter provides an overview of digitalization and automation along with the three underlying technologies and explores the potential of these technologies to replace human capabilities in the workplace. Subsequently, it discusses a set of factors beyond technological feasibility that influence the pace and scope of job automation. Some of the chapter’s key findings include the following: (1) The majority of jobs will be affected by the automation of individual activities, but only a few have the potential to be completely substituted; (2) the automation potential for non-routine tasks seems to remain limited, especially for tasks involving autonomous mobility, creativity, problem-solving and complex communication; (3) the nature of jobs will change as mundane tasks will be substituted and people will work more closely together with machines; and (4) industries that have a large potential for labor substitution are food and accommodation services, transportation and warehousing, retail trade, wholesale trade and manufacturing

Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems

This book explores the critical role of acquisition, application, enhancement, and management of knowledge and human competence in the context of the largely digital and data/information dominated modern world. Whilst humanity owes much of its achievements to the distinct capability to learn from observation, analyse data, gain insights, and perceive beyond original realities, the systematic treatment of knowledge as a core capability and driver of success has largely remained the forte of pedagogy. In an increasingly intertwined global community faced with existential challenges and risks, the significance of knowledge creation, innovation, and systematic understanding and treatment of human competence is likely to be humanity's greatest weapon against adversity. This book was conceived to inform the decision makers and practitioners about the best practice pertinent to many disciplines and sectors. The chapters fall into three broad categories to guide the readers to gain insight from generic fundamentals to discipline-specific case studies and of the latest practice in knowledge and competence management

Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems

This book explores the critical role of acquisition, application, enhancement, and management of knowledge and human competence in the context of the largely digital and data/information dominated modern world. Whilst humanity owes much of its achievements to the distinct capability to learn from observation, analyse data, gain insights, and perceive beyond original realities, the systematic treatment of knowledge as a core capability and driver of success has largely remained the forte of pedagogy. In an increasingly intertwined global community faced with existential challenges and risks, the significance of knowledge creation, innovation, and systematic understanding and treatment of human competence is likely to be humanity's greatest weapon against adversity. This book was conceived to inform the decision makers and practitioners about the best practice pertinent to many disciplines and sectors. The chapters fall into three broad categories to guide the readers to gain insight from generic fundamentals to discipline-specific case studies and of the latest practice in knowledge and competence management

Information-theoretic Reasoning in Distributed and Autonomous Systems

The increasing prevalence of distributed and autonomous systems is transforming decision making in industries as diverse as agriculture, environmental monitoring, and healthcare. Despite significant efforts, challenges remain in robustly planning under uncertainty. In this thesis, we present a number of information-theoretic decision rules for improving the analysis and control of complex adaptive systems. We begin with the problem of quantifying the data storage (memory) and transfer (communication) within information processing systems. We develop an information-theoretic framework to study nonlinear interactions within cooperative and adversarial scenarios, solely from observations of each agent's dynamics. This framework is applied to simulations of robotic soccer games, where the measures reveal insights into team performance, including correlations of the information dynamics to the scoreline. We then study the communication between processes with latent nonlinear dynamics that are observed only through a filter. By using methods from differential topology, we show that the information-theoretic measures commonly used to infer communication in observed systems can also be used in certain partially observed systems. For robotic environmental monitoring, the quality of data depends on the placement of sensors. These locations can be improved by either better estimating the quality of future viewpoints or by a team of robots operating concurrently. By robustly handling the uncertainty of sensor model measurements, we are able to present the first end-to-end robotic system for autonomously tracking small dynamic animals, with a performance comparable to human trackers. We then solve the issue of coordinating multi-robot systems through distributed optimisation techniques. These allow us to develop non-myopic robot trajectories for these tasks and, importantly, show that these algorithms provide guarantees for convergence rates to the optimal payoff sequence