Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Reinforcement Learning Approaches in Social Robotics

This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field

Biosignal‐based human–machine interfaces for assistance and rehabilitation : a survey

As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal‐based HMIs for assistance and rehabilitation to outline state‐of‐the‐art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full‐text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever‐growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complex-ity, so their usefulness should be carefully evaluated for the specific application

Fuzzy Mouse Cursor Control System for Computer Users with Spinal Cord Injuries

People with severe motor-impairments due to Spinal Cord Injury (SCI) or Spinal Cord Dysfunction (SCD), often experience difficulty with accurate and efficient control of pointing devices (Keates et al., 02). Usually this leads to their limited integration to society as well as limited unassisted control over the environment. The questions “How can someone with severe motor-impairments perform mouse pointer control as accurately and efficiently as an able-bodied person?” and “How can these interactions be advanced through use of Computational Intelligence (CI)?” are the driving forces behind the research described in this paper. Through this research, a novel fuzzy mouse cursor control system (FMCCS) is developed. The goal of this system is to simplify and improve efficiency of cursor control and its interactions on the computer screen by applying fuzzy logic in its decision-making to make disabled Internet users use the networked computer conveniently and easily. The FMCCS core consists of several fuzzy control functions, which define different user interactions with the system. The development of novel cursor control system is based on utilization of motor functions that are still available to most complete paraplegics, having capability of limited vision and breathing control. One of the biggest obstacles of developing human computer interfaces for disabled people focusing primarily on eyesight and breath control is user’s limited strength, stamina, and reaction time. Within the FMCCS developed in this research, these limitations are minimized through the use of a novel pneumatic input device and intelligent control algorithms for soft data analysis, fuzzy logic and user feedback assistance during operation. The new system is developed using a reliable and cheap sensory system and available computing techniques. Initial experiments with healthy and SCI subjects have clearly demonstrated benefits and promising performance of the new system: the FMCCS is accessible for people with severe SCI; it is adaptable to user specific capabilities and wishes; it is easy to learn and operate; point-to-point movement is responsive, precise and fast. The integrated sophisticated interaction features, good movement control without strain and clinical risks, as well the fact that quadriplegics, whose breathing is assisted by a respirator machine, still possess enough control to use the new system with ease, provide a promising framework for future FMCCS applications. The most motivating leverage for further FMCCS development is however, the positive feedback from persons who tested the first system prototype

A motor imagery based brain-computer interface system via swarm-optimized fuzzy integral and its application

© 2016 IEEE. A brain-computer interface (BCI) system provides a convenient means of communication between the human brain and a computer, which is applied not only to healthy people but also for people that suffer from motor neuron diseases (MNDs). Motor imagery (MI) is one well-known basis for designing Electroencephalography (EEG)-based real-life BCI systems. However, EEG signals are often contaminated with severe noise and various uncertainties, imprecise and incomplete information streams. Therefore, this study proposes spectrum ensemble based on swam-optimized fuzzy integral for integrating decisions from sub-band classifiers that are established by a sub-band common spatial pattern (SBCSP) method. Firstly, the SBCSP effectively extracts features from EEG signals, and thereby the multiple linear discriminant analysis (MLDA) is employed during a MI classification task. Subsequently, particle swarm optimization (PSO) is used to regulate the subject-specific parameters for assigning optimal confidence levels for classifiers used in the fuzzy integral during the fuzzy fusion stage of the proposed system. Moreover, BCI systems usually tend to have complex architectures, be bulky in size, and require time-consuming processing. To overcome this drawback, a wireless and wearable EEG measurement system is investigated in this study. Finally, in our experimental result, the proposed system is found to produce significant improvement in terms of the receiver operating characteristic (ROC) curve. Furthermore, we demonstrate that a robotic arm can be reliably controlled using the proposed BCI system. This paper presents novel insights regarding the possibility of using the proposed MI-based BCI system in real-life applications