A Computational Approach To Human Adaptive Motor Control

Human Motor Control is a elementary introduction to the field of motor control, stressing psychological, physiological, and computational approaches. Human Motor Control cuts across all disciplines which are defined with respect to movement: physical education, dance, physical therapy, robotics, and so on. The book is organized around major activity areas. A comprehensive presentation of the major problems and topics in human motor control Incorporates applications of work that lie outside traditional sports or physical education teaching

This volume is the most recent installment of the Progress in Motor Control series. It contains contributions based on presentations by invited speakers at the Progress in Motor Control VIII meeting held in Cincinnati, OH, USA in July, 2011. Progress in Motor Control is the official scientific meeting of the International Society of Motor Control (ISMC). The Progress in Motor Control VIII meeting, and consequently this volume, provide a broad perspective on the latest research on motor control in humans and other species.

Motor Behavior and Human Skill details the most recent research in motor control and human skill. The book provides a forum for the analysis of the many diverse theoretical approaches used in the understanding of motor control, including the cognitive, dynamical systems, computational, and neurological approaches.

A synthesis of biomechanics and neural control that draws on recent advances in robotics to address control problems solved by the human sensorimotor system. This book proposes a transdisciplinary approach to investigating human motor control that synthesizes musculoskeletal biomechanics and neural control. The authors argue that this integrated approach—which uses the framework of robotics to understand sensorimotor control problems—offers a more complete and accurate description than either a purely neural computational approach or a purely biomechanical one. The authors offer an account of motor control in which explanatory models are based on experimental evidence using mathematical approaches reminiscent of physics. These computational models yield algorithms for motor control that may be used as tools to investigate or treat diseases of the sensorimotor system and to guide the development of algorithms and hardware that can be incorporated into products designed to assist with the tasks of daily living. The authors focus on the insights their approach offers in understanding how movement of the arm is controlled and how the control adapts to changing environments. The book begins with muscle mechanics and control, progresses in a logical manner to planning and behavior, and describes applications in neurorehabilitation and robotics. The material is self-contained, and accessible to researchers and professionals in a range of fields, including psychology, kinesiology, neurology, computer science, and robotics.

In this thesis, computational models of adaptive motor control and visuomotor coordination are explored and developed. These models relate to hypotheses on how sensorimotor processing in biological organisms might be organized at an abstract level; furthermore, these models and their specific implementations offer solutions for technical problems in the domain of adaptive robotics. For this reason, both biological and technical aspects are addressed. On the one hand, this thesis focuses on the learning of so-called internal models (Miall et al., 1993; Kawato, 1999): "forward models", which predict the sensory consequences of the agent''s own actions, and "inverse models", which act like motor controllers and generate motor commands. In this area, new strategies and algorithms for learning are suggested and tested on both simulated and real-world robot setups. This work contributes to the understanding of the "building blocks" of integrated sensorimotor processing. On the other hand, this thesis suggests complex models of sensorimotor coordination: In a study on the grasping to extrafoveal targets with a robot arm, it is explored how forward and inverse models may interact, and a second study addresses the question how visual perception of space might arise from the learning of sensorimotor relationships. The theoretical part of the thesis starts with a close view on sensorimotor processing. The cognitivist approach and the embodied approach to sensorimotor processing are contrasted with each other, providing evidence from psychological and neurophysiological studies in favor of the latter. It is outlined how the application of robots fits into the embodied approach as research method. Furthermore, internal models are defined in a formal way, and an overview of their role in models of perception and cognition is provided, with a special emphasis on anticipation and predictive forward models. Afterwards, a thorough overview of internal models in adaptive motor control (covering both kinematics and dynamics) and a novel learning strategy for kinematic control problems ("learning by averaging") are presented. The experimental work comprises four different studies. First, a detailed comparison study of various motor learning strategies for kinematic problems is presented. The performance of "feedback error learning" (Kawato et al., 1987), "distal supervised learning" (Jordan and Rumelhart, 1992), and "direct inverse modeling" (e.g., Kuperstein, 1987) is directly compared on several learning tasks from the domain of eye and arm control (on simulated setups). Moreover, an improved version of direct inverse modeling on the basis of abstract recurrent networks and learning by averaging are included in the comparison. The second study is dedicated to the learning of a visual forward model for a robot camera head. This forward model predicts the visual consequences of camera movements for all pixels of the camera image. The presented learning algorithm is able to overcome the two main difficulties of visual prediction: first, the high dimensionality of the input and output space, and second, the need to detect which part of the visual output is non-predictable. To demonstrate the robustness of the presented learning algorithm, the work is not carried out on plain camera images, but on distorted "retinal images" with a decreasing resolution towards the corners. In the third experimental chapter, a model for grasping to extrafoveal (non-fixated) targets is presented. It is implemented on a robot setup, consisting of a camera head and a robot arm. This model is based on the premotor theory of attention (Rizzolatti et al., 1994) and adds one specific hypothesis: Attention shifts caused by saccade programming imply a prediction of the retinal foveal images after the saccade. For this purpose, the visual forward model from the preceding study is used. Based on this model, several grasping modes are compared; the obtained results are qualitatively congruent with the performance that can be expected from human subjects. The fourth study is based on the theory that visual perception of space and shape is based on an internal simulation process which relies on forward models (Moeller, 1999). This theory is tested by synthetic modeling in the task domain of block pushing with a robot arm.

This volume evolved from a workshop which addressed the general area of motor control, and the broader problems of serial organisation and sensory-motor integration of human skills. A number of specific issues are highlighted, including the neural mechanisms and disabilities of sensory-motor integration, planning and programming of action, the dynamics of interlimb coordination, amendment and updating mechanisms, and in particular, perception-action coupling and the representation of action. Underlying much of the volume are the major theoretical issues which include the debate between computational and prescriptive approaches versus the emergent properties and system dynamics approaches. The book represents a diverse approach from such disciplines as psychology, electrical and mechanical engineering, human movement studies, physiotherapy, neurology, and kinesiology.

During the past two decades, there has been a dramatic increase in interest in the study of motor control and learning. In this volume authors from a variety of backgrounds and theoretical perspectives review their research with particular emphasis on the methods and paradigms employed, and the future direction of their work. The book is divided into four main sections. The first section contains chapters examining general issues and trends in the movement behaviour field. The remaining three sections contain chapters from scientists working in three broadly defined areas of interest: coordination and control; visuo-motor processes; and movement disorders. Each section provides an overview of the different approaches and different levels of analysis being used to examine specific topics within the motor domain.

In the study of the computational structure of biological/robotic sensorimotor systems, distributed models have gained center stage in recent years, with a range of issues including self-organization, non-linear dynamics, field computing etc. This multidisciplinary research area is addressed here by a multidisciplinary team of contributors, who provide a balanced set of articulated presentations which include reviews, computational models, simulation studies, psychophysical, and neurophysiological experiments. The book is divided into three parts, each characterized by a slightly different focus: in part I, the major theme concerns computational maps which typically model cortical areas, according to a view of the sensorimotor cortex as "geometric engine" and the site of "internal models" of external spaces. Part II also addresses problems of self-organization and field computing, but in a simpler computational architecture which, although lacking a specialized cortical machinery, can still behave in a very adaptive and surprising way by exploiting the interaction with the real world. Finally part III is focused on the motor control issues related to the physical properties of muscular actuators and the dynamic interactions with the world. The reader will find different approaches on controversial issues, such as the role and nature of force fields, the need for internal representations, the nature of invariant commands, the vexing question about coordinate transformations, the distinction between hierachiacal and bi-directional modelling, and the influence of muscle stiffness.