Explore the vast range of titles available.

\"This book discusses the radical technological changes occurring due to Industry 4.0, with a focus on offering a better understanding of the Fourth Industrial Revolution. It also presents a detailed analysis of interdisciplinary knowledge, numerical modeling and simulation, and the application of cyber-physical systems, where information technology and physical devices create synergic systems leading to unprecedented efficiency. The theoretical results, practical solutions, and guidelines presented are valuable for both researchers working in the area of engineering sciences and practitioners looking for solutions to industrial problems\"-- Provided by publisher.

[This corrects the article DOI: 10.1371/journal.pone.0333616.].

The inverse in crisp graph theory is a well-known topic. However, the inverse concept for fuzzy graphs has recently been created, and its numerous characteristics are being examined. Each node and edge in m-polar fuzzy graphs (mPFG) include m components, which are interlinked through a minimum relationship. However, if one wants to maximize the relationship between nodes and edges, then the m-polar fuzzy graph concept is inappropriate. Considering everything we wish to obtain here, we present an inverse graph under an m-polar fuzzy environment. An inverse mPFG is one in which each component’s membership value (MV) is greater than or equal to that of each component of the incidence nodes. This is in contrast to an mPFG, where each component’s MV is less than or equal to the MV of each component’s incidence nodes. An inverse mPFG’s characteristics and some of its isomorphic features are introduced. The α-cut concept is also studied here. Here, we also define the composition and decomposition of an inverse mPFG uniquely with a proper explanation. The connectivity concept, that is, the strength of connectedness, cut nodes, bridges, etc., is also developed on an inverse mPF environment, and some of the properties of this concept are also discussed in detail. Lastly, a real-life application based on the robotics manufacturing allocation problem is solved with the help of an inverse mPFG.

To expand the use of robots to assist and replace workers in tasks, the robot needs to deal with not only repetitive and simple tasks but also complex and delicate tasks with high speed and high accuracy. In recent years, imitation learning has been used in several studies to enable robots to learn complex human-like motion with little learning cost. However, in the imitation learning framework, it is difficult to make teaching data that takes into account optimal acceleration/deceleration, force, and constraints of the robot from a control perspective. In this paper, we propose a control scheme to track a fast and smooth imitation motion by implementing a model predictive control (MPC) scheme. To accelerate and smooth human teaching motions, we designed an MPC that follows a reference trajectory output from a motion generator learned by using deep predictive learning (DPL). By adopting this approach, it is possible to suppress excessive accelerations and decelerations while maintaining the ability to follow the target imitation motion. This allows for an increase in the robot’s motion speed while preserving the task success rate. Through simulations of an object grasping task and actual environments of a door-opening task, we evaluated the effectiveness of the proposed control scheme.

[This corrects the article DOI: 10.1371/journal.pone.0303704.].

Technology-assisted clinical interventions are increasingly common in the health care field, often with the proposed aim to improve access to and cost-effectiveness of care. Current technology platforms delivering interventions are largely mobile apps and online websites, although efforts have been made to create more personalized and embodied technology experiences. To extend and improve on these platforms, the field of robotics has been increasingly included in conversations of how to deliver technology-assisted, interactive, and responsive mental health and psychological well-being interventions. Socially assistive robots (SARs) are robotic technology platforms with audio, visual, and movement capabilities that are being developed to interact with individuals socially while also assisting them with management of their physical and psychological well-being. However, little is known about the empirical evidence or utility of using SARs in mental health interventions. The review synthesizes and describes the nascent empirical literature of SARs in mental health research and identifies strengths, weaknesses, and opportunities for improvement in future research and practice. Searches in Medline, PsycINFO, PsycARTICLES, PubMed, and IEEE Xplore yielded 12 studies included in the final review after applying inclusion and exclusion criteria. Abstract and full-text reviews were conducted by two authors independently. This systematic review of the literature found 5 distinct SARs used in research to investigate the potential for this technology to address mental health and psychological well-being outcomes. Research on mental health applications of SARs focuses largely on elderly dementia patients and relies on usability pilot data with methodological limitations. The current SARs research in mental health use is limited in generalizability, scope, and measurement of psychological outcomes. Opportunities for expansion of research in this area include diversifying populations studied, SARs used, clinical applications, measures used, and settings for those applications.

Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin by outlining the fundamental ideas of reinforcement learning and the parts of a reinforcement learning system. The many deep reinforcement learning algorithms, such as value-based methods, policy-based methods, and actor–critic approaches, that have been suggested for robotic manipulation tasks are then covered. We also examine the numerous issues that have arisen when applying these algorithms to robotics tasks, as well as the various solutions that have been put forth to deal with these issues. Finally, we highlight several unsolved research issues and talk about possible future directions for the subject.

A humanoid robot’s highly anticipated debut quickly went awry when it stumbled onstage at a technology showcase in Moscow on Nov. 11.