Editorial: Socio-technical ecologies: Design for human-machine systems

[EN] Computer system design has been driven predominantly by technical aspects and considerations. However, as computing systems are increasingly embedded and integrated into the world, they have an extended impact on individuals and societies. In this context, social and behavioral dynamics become equally essential components of computing systems research, notably including robotics and artificial intelligence. The increasing shift towards socio-technical systems and the integration of collaborative and cooperative aspects require new multidisciplinary efforts and a focus on the larger system context and evolution. In hybrid systems, i.e., artificial agents, robots, and humans interacting with each other, explicitly considering the underlying social relations and dynamics is integral to the ability to design robust, adaptive, purpose- and useful systems. Recent advances in machine learning and other computational techniques allow for the effective real-time analysis of complex interaction behavior. This holds both for the individual constituents as well as for the integration of subsystems. This highly multidisciplinary Research Topic, therefore, spans various journals including Frontiers in Psychology and Frontiers in Computer Science with their Human-Media Interaction sections, Frontiers in Robotics and AI with its Human-Robot Interaction and MultiRobot Systems sections, and Frontiers in Artificial Intelligence with its AI for Human Learning and Behavior Change section.S

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

AI, Robotics, and the Future of Jobs

This report is the latest in a sustained effort throughout 2014 by the Pew Research Center's Internet Project to mark the 25th anniversary of the creation of the World Wide Web by Sir Tim Berners-Lee (The Web at 25).The report covers experts' views about advances in artificial intelligence (AI) and robotics, and their impact on jobs and employment

Conditional Task and Motion Planning through an Effort-based Approach

This paper proposes a preliminary work on a Conditional Task and Motion Planning algorithm able to find a plan that minimizes robot efforts while solving assigned tasks. Unlike most of the existing approaches that replan a path only when it becomes unfeasible (e.g., no collision-free paths exist), the proposed algorithm takes into consideration a replanning procedure whenever an effort-saving is possible. The effort is here considered as the execution time, but it is extensible to the robot energy consumption. The computed plan is both conditional and dynamically adaptable to the unexpected environmental changes. Based on the theoretical analysis of the algorithm, authors expect their proposal to be complete and scalable. In progress experiments aim to prove this investigation