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

Human-robot interaction for assistive robotics

This dissertation presents an in-depth study of human-robot interaction (HRI) withapplication to assistive robotics. In various studies, dexterous in-hand manipulation is included, assistive robots for Sit-To-stand (STS) assistance along with the human intention estimation. In Chapter 1, the background and issues of HRI are explicitly discussed. In Chapter 2, the literature review introduces the recent state-of-the-art research on HRI, such as physical Human-Robot Interaction (HRI), robot STS assistance, dexterous in hand manipulation and human intention estimation. In Chapter 3, various models and control algorithms are described in detail. Chapter 4 introduces the research equipment. Chapter 5 presents innovative theories and implementations of HRI in assistive robotics, including a general methodology of robotic assistance from the human perspective, novel hardware design, robotic sit-to-stand (STS) assistance, human intention estimation, and control

Extraction of Nonlinear Synergies for Proportional and Simultaneous Estimation of Finger Kinematics

Objective: Proportional and simultaneous estimation of finger kinematics from surface EMG based on the assumption that there exists a correlation between muscle activations and finger kinematics in low dimensional space. Methods: We employ Manifold Relevance Determination (MRD), a multi-view learning model with a nonparametric Bayesian approach, to extract the nonlinear muscle and kinematics synergies and the relationship between them by studying muscle activations (input-space) together with the finger kinematics (output-space). Results: This study finds that there exist muscle synergies which are associated with kinematic synergies. The acquired nonlinear synergies and the association between them has further been utilized for the estimation of finger kinematics from muscle activation inputs, and the proposed approach has outperformed other commonly used linear and nonlinear regression approaches with an average correlation coefficient of 0.91±0.03. Conclusion: There exists an association between muscle and kinematic synergies which can be used for the proportional and simultaneous estimation of finger kinematics from the muscle activation inputs. Significance: The findings of this study not only presents a viable approach for accurate and intuitive myoelectric control but also provides a new perspective on the muscle synergies in the motor control community

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

This eBook provides a comprehensive treatise on modern biomechatronic systems centred around human applications. A particular emphasis is given to exoskeleton designs for assistance and training with advanced interfaces in human-machine interaction. Some of these designs are validated with experimental results which the reader will find very informative as building-blocks for designing such systems. This eBook will be ideally suited to those researching in biomechatronic area with bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design at post-graduate level

Progress and Prospects of the Human-Robot Collaboration

International audienceRecent technological advances in hardware designof the robotic platforms enabled the implementationof various control modalities for improved interactions withhumans and unstructured environments. An important applicationarea for the integration of robots with such advancedinteraction capabilities is human-robot collaboration. Thisaspect represents high socio-economic impacts and maintainsthe sense of purpose of the involved people, as the robotsdo not completely replace the humans from the workprocess. The research community’s recent surge of interestin this area has been devoted to the implementation of variousmethodologies to achieve intuitive and seamless humanrobot-environment interactions by incorporating the collaborativepartners’ superior capabilities, e.g. human’s cognitiveand robot’s physical power generation capacity. In fact,the main purpose of this paper is to review the state-of-thearton intermediate human-robot interfaces (bi-directional),robot control modalities, system stability, benchmarking andrelevant use cases, and to extend views on the required futuredevelopments in the realm of human-robot collaboration

A human‐robot collaboration method for uncertain surface scanning

Robots are increasingly expected to replace humans in many repetitive and high‐precision tasks, of which surface scanning is a typical example. However, it is usually difficult for a robot to independently deal with a surface scanning task with uncertainties in, for example the irregular surface shapes and surface properties. Moreover, it usually requires surface modelling with additional sensors, which might be time‐consuming and costly. A human‐robot collaboration‐based approach that allows a human user and a robot to assist each other in scanning uncertain surfaces with uniform properties, such as scanning human skin in ultrasound examination is proposed. In this approach, teleoperation is used to obtain the operator's intent while allowing the operator to operate remotely. After external force perception and friction estimation, the orientation of the robot end‐effector can be autonomously adjusted to keep as perpendicular to the surface as possible. Force control enables the robotic manipulator to maintain a constant contact force with the surface. And hybrid force/motion control ensures that force, position, and pose can be regulated without interfering with each other while reducing the operator's workload. The proposed method is validated using the Elite robot to perform a mock B‐ultrasound scanning experiment

2022 roadmap on neuromorphic computing and engineering

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10$^{18}$ calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community

比例筋電位制御に向けた筋シナジーの抽出、解釈、および応用の研究

Transfer of human intentions into myoelectric hand prostheses is generally achieved by learning a mapping, directly from sEMG signals to the Kinematics using linear or nonlinear regression approaches. Due to the highly random and nonlinear nature of sEMG signals such approaches are not able to exploit the functions of the modern pros- thesis, completely. Inspired from the muscle synergy hypothesis in the motor control community, some studies in the past have shown that better estimation accuracies can be achieved by learning a mapping to kinematics space from the synergistic features extracted from sEMG. However, mainly linear algorithms such as Principle Compo- nent Analysis (PCA), and Non-negative matrix factorization (NNMF) were employed to extract synergistic features, separately, from EMG and kinematics data and have not considered the nonlinearity and the strong correlation that exist between finger kine- matics and muscles. To exploit the relationship between EMG and Finger Kinematics for myoelectric control, we propose the use of the Manifold Relevance Determination (MRD) model (multi-view learning) to find the correspondence between muscular and kinematics by learning a shared low-dimensional representation. In the first part of the study, we present the approach of multi-view learning, interpretation of extracted non- linear muscle synergies from the joint study of sEMG and finger kinematics and their use in estimating the finger kinematics for the upper-limb prosthesis. Applicability of the proposed approach is then demonstrated by comparing the kinematics estimation accuracies against linear synergies and direct mapping. In the second part of the study, we propose a new approach to extract nonlinear muscle synergies from sEMG using multiview learning which addresses the two main drawbacks (1. Inconsistent synergistic patterns upon addition of sEMG signals from more muscles, 2. Weak metric for accessing the quality and quantity of muscle synergies) of established algorithms and discuss the potential of the proposed approach for reducing the number of electrodes with negligible degradation in predicted kinematics.九州工業大学博士学位論文 学位記番号：生工博甲第372号 学位授与年月日：令和2年3月25日1 Introduction|2 Related Work|3 Extraction of nonlinear synergies for proportional and simultaneous estimation of finger kinematics|4 An Approach to Extract Nonlinear Muscle Synergies from sEMG through Multi-Model Learning|5 Conclusion and Future Work九州工業大学令和元年

Human Machine Interaction

In this book, the reader will find a set of papers divided into two sections. The first section presents different proposals focused on the human-machine interaction development process. The second section is devoted to different aspects of interaction, with a special emphasis on the physical interaction