| 书目名称 | Human and Robot Hands | | 副标题 | Sensorimotor Synergi | | 编辑 | Matteo Bianchi,Alessandro Moscatelli | | 视频video | | | 概述 | Presents an integrated approach between neuroscience and robotics to understand the complex issue of sensorimotor control of the hand.Enriches the understanding of synergies as a framework for an effi | | 丛书名称 | Springer Series on Touch and Haptic Systems | | 图书封面 |  | | 描述 | .This book looks at the common problems both human and robotic hands encounter when controlling the large number of joints, actuators and sensors required to efficiently perform motor tasks such as object exploration, manipulation and grasping. The authors adopt an integrated approach to explore the control of the hand based on sensorimotor synergies that can be applied in both neuroscience and robotics. Hand synergies are based on goal-directed, combined muscle and kinematic activation leading to a reduction of the dimensionality of the motor and sensory space, presenting a highly effective solution for the fast and simplified design of artificial systems...Presented in two parts, the first part, .Neuroscience,. provides the theoretical and experimental foundations to describe the synergistic organization of the human hand. The second part, .Robotics, Models and Sensing Tools., exploits the framework of hand synergies to better control and design robotic hands and haptic/sensing systems/tools, using a reduced number of control inputs/sensors, with the goal of pushing their effectiveness close to the natural one...Human and Robot Hands .provides a valuable reference for students, | | 出版日期 | Book 2016 | | 关键词 | Robotic hands; Sensing Systems; Human Touch; Sensing Modeling; Human Hand Modeling | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-26706-7 | | isbn_softcover | 978-3-319-80001-1 | | isbn_ebook | 978-3-319-26706-7Series ISSN 2192-2977 Series E-ISSN 2192-2985 | | issn_series | 2192-2977 | | copyright | Springer International Publishing AG, part of Springer Nature 2016 |
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Front Matter |
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Abstract
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,Introduction, |
Matteo Bianchi,Alessandro Moscatelli |
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Abstract
The human hand is our preeminent and most versatile tool to explore and modify the external environment. It represents both the cognitive organ of the sense of touch and the most important end effector in object manipulation and grasping. Our brain can cope efficiently with the high degree of complexity of the hand, which arises from the huge amount of actuators and sensors. This allows us to perform a large number of daily life tasks, from the simple ones, such as determining the ripeness of a fruit or drive a car, to the more complex ones, as for example performing surgical procedures, playing an instrument or painting. Not surprisingly, an intensive research effort has been devoted to (i) understand the neuroanatomical and physiological mechanisms underpinning the sensorimotor control of human hands and (ii) to attempt to reproduce such mechanisms in artificial robotic systems.
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Abstract
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Dexterous Manipulation: From High-Level Representation to Low-Level Coordination of Digit Forces and |
Qiushi Fu,Marco Santello |
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Abstract
The ability to perform fine object and tool manipulation, a hallmark of human dexterity, is not well understood. We have been studying how humans learn anticipatory control of manipulation tasks to characterize the mechanisms underlying the transformation from multiple sources of sensory feedback to the coordination of multiple degrees of freedom of the hand. In our approach, we have removed constraints on digit placement to study how subjects explore and choose relations between digit forces and positions. It was found that the digit positions were characterized by high trial-to-trial variability, thus challenging the extent to which the Central Nervous System (CNS) could have relied on sensorimotor memories built through previous manipulations for anticipatory control of digit forces. Importantly, subjects could adjust digit forces prior to the onset of manipulation to compensate for digit placement variability, thus leading to consistent outcome at the task level. Furthermore, we found that manipulation learned with a set of digits can be transferred to grips involving a different number of digits, despite the significant change in digit placement distribution. These results hav
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Digit Position and Force Synergies During Unconstrained Grasping |
Abdeldjallil Naceri,Marco Santello,Alessandro Moscatelli,Marc O. Ernst |
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Abstract
Grasping is a complex motor task which requires a fine control of the multiple degrees of freedom of the hand, in both the position and the force domain. In this chapter, we investigated the coordinated control of digit position and force in the human hand while grasping and holding a moving object. We observed a substantial variability between participants in the hand posture. Instead, digit placement was rather stereotyped for repeated grasps of the same participant. The normal forces applied by the digits co-varied with their placement across trials. Specifically, we observed an exponential relationship between finger placement and normal force applied for the thumb and lateral fingers. For the middle and ring fingers, the force responses co-varied in an approximately linear fashion with digit position. Principal component analysis revealed that more than 97 % of the finger force variance was accounted by the first two components (corresponding to the first and the second force synergy). This is consisted with the framework of motor synergy, since two components successfully explained most of the variability in the data.
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The Motor Control of Hand Movements in the Human Brain: Toward the Definition of a Cortical Represen |
Andrea Leo,Giacomo Handjaras,Hamal Marino,Matteo Bianchi,Pietro Pietrini,Emiliano Ricciardi |
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Abstract
The control of the many degrees of freedom of the hand through functional modules (hand synergies) has been proposed as a potentially useful model to describe how the hand can maintain postures while being able to rapidly change its configuration to accomplish a wide range of tasks. However, whether and to what extent synergies are actually encoded in motor cortical areas is still debated. A direct encoding of hand synergies is suggested by electrophysiological studies in nonhuman primates, but the evidence in humans resulted, so far, partial and indirect. In this chapter, we review the organization of the brain network that controls hand posture in humans and present preliminary results of a functional Magnetic Resonance Imaging (fMRI) on the encoding of synergies at a cortical level to control hand posture in humans.
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Synergy Control in Subcortical Circuitry: Insights from Neurophysiology |
Henrik Jörntell |
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Abstract
Synergy control in the brain is likely to a large extent delegated to subcortical circuitry, with a focus on spinal circuitry and add-on capability provided by the spinocerebellar system for complex synergies. The advantage with this organization is that there is a tight connection, in the sense of shorter delays, between sensor feedback and the continuously updated motor command. By involving the sensory feedback in the motor command, the brain can make sure that the relevant biomechanical properties are properly compensated for. A consequence of this arrangement is that the neocortex, from which all voluntary motor commands originates, needs to learn the properties of the subcortical circuitry rather than the full details of the high-dimensional biomechanical plant. As the subcortical circuitry appears to have primarily linear properties, this arrangement makes it possible for the voluntary system to add synergy components linearly.
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Neuronal “Op-amps” Implement Adaptive Control in Biology and Robotics |
Martin Nilsson |
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Abstract
Animals control their limbs very efficiently using interconnected neuronal populations. We propose that these populations can be seen as general-purpose ., or neuronal “op-amps”, forming adaptive feedback networks. The neuronal op-amp is an interdisciplinary concept offering tentative explanations of animal behaviour as well as approaches to biologically inspired high-dimensional robot control. For instance, in biology, the concept indicates the origin of synergies and saliency in the mammalian central nervous system; in robotics, it presents a design of simple but robust adaptive controllers that identify unknown sensors online. Here, we introduce the neuronal op-amp concept and its biological basis. We explore its biological plausibility, its application, and its performance in adaptive control both theoretically and experimentally.
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Sensorymotor Synergies: Fusion of Cutaneous Touch and Proprioception in the Perceived Hand Kinematic |
Alessandro Moscatelli,Matteo Bianchi,Alessandro Serio,Antonio Bicchi,Marc O. Ernst |
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Abstract
According to classical studies in physiology, muscle spindles and other receptors from joints and tendons provide crucial information on the position of our body and our limbs. Cutaneous cues also provide an important contribution to our sense of position. For example, it is possible to induce a vivid sensation of movement in the anesthetized finger, by stretching the skin around the proximal interphalangeal joint. However, much of proprioceptive literature did not consider the role of tactile interaction with external objects as position and motion cues. Whenever we touch an external, stationary object, the contact forces produce a mechanical deformation of the skin which changes with the hand posture and movement. Therefore, these cutaneous contact cues might also provide proprioceptive information. In this paragraph, evaluated this hypothesis based on recently published experimental data.
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Abstract
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From Soft to Adaptive Synergies: The Pisa/IIT SoftHand |
Manuel G. Catalano,Giorgio Grioli,Edoardo Farnioli,Alessandro Serio,Manuel Bonilla,Manolo Garabini,C |
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Abstract
Taking inspiration from the neuroscientific findings on hand synergies discussed in the first part of the book, in this chapter we present the Pisa/IIT SoftHand, a novel robot hand prototype. The design moves under the guidelines of making an hardware robust and easy to control, preserving an high level of grasping capabilities and an aspect as similar as possible to the human counterpart. First, the main theoretical tools used to enable such simplification are presented, as for example the notion of .. A discussion of some possible actuation schemes shows that a straightforward implementation of the soft synergy idea in an effective design is not trivial. The proposed approach, called ., rests on ideas coming from underactuated hand design, offering a design method to implement the desired set of soft synergies as demonstrated both with simulations and experiments. As a particular instance of application of the synthesis method of adaptive synergies, the Pisa/IIT SoftHand is described in detail. The hand has 19 joints, but only uses one actuator to activate its adaptive synergy. Of particular relevance in its design is the very soft and safe, yet powerful and extremely robust stru
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A Learn by Demonstration Approach for Closed-Loop, Robust, Anthropomorphic Grasp Planning |
Minas V. Liarokapis,Charalampos P. Bechlioulis,George I. Boutselis,Kostas J. Kyriakopoulos |
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Abstract
This chapter presents a learn by demonstration approach, for closed-loop, robust, anthropomorphic grasp planning. In this respect, human demonstrations are used to perform skill transfer between the human and the robot artifacts, mapping human to robot motion with functional anthropomorphism [.]. In this work we extend the synergistic description adopted in Chaps. .–. for human grasping, in Chap. . for robotic hand design and, finally, in Chap. . for hand pose reconstruction systems, to define a low-dimensional manifold where the extracted anthropomorphic robot arm hand system kinematics are projected and appropriate Navigation Function (NF) models are trained. The training of the NF models is performed in a task-specific manner, for various: (1) subspaces, (2) objects and (3) tasks to be executed with the corresponding object. A vision system based on RGB-D cameras (Kinect, Microsoft) provides online feedback, performing object detection, object pose estimation and triggering the appropriate NF models. The NF models formulate a closed-loop velocity control scheme, that ensures humanlikeness of robot motion and guarantees convergence to the desired goals. The aforementioned scheme
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Teleimpedance Control: Overview and Application |
Arash Ajoudani,Sasha B. Godfrey,Nikos Tsagarakis,Antonio Bicchi |
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Abstract
In previous chapters, human hand and arm kinematics have been analyzed through a synergstic approach and the underlying concepts were used to design robotic systems and devise simplified control algorithms. On the other hand, it is well-known that synergies can be studied also at a muscular level as a coordinated activation of multiple muscles acting as a single unit to generate different movements. As a result, muscular activations, quantified through Electromyography (EMG) signals can be then processed and used as direct inputs to external devices with a large number of DOFs. In this chapter, we present a minimalistic approach based on tele-impedance control, where EMGs from only one pair of antagonistic muscle pair are used to map the users postural and stiffness references to the synergy-driven anthropomorphic robotic hand, described in Chap. .. In this direction, we first provide an overview of the teleimpedance control concept which forms the basis for the development of the hand controller. Eventually, experimental results evaluate the effectiveness of the teleimpedance control concept in execution of the tasks which require significant dynamics variation or are executed in
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Incremental Learning of Muscle Synergies: From Calibration to Interaction |
Claudio Castellini |
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Abstract
In the previous chapter it has been shown how sEMG gathered from only two loci of muscular activity with opposite mechanical actions can be used to control the synergy-inspired robotic hand described in Chap. .. Here, the problem of simplifying the control of a multi-DOF, multi-DOA mechatronic system—more specifically a prosthetic hand—is tackled from the opposite perspective, i.e. by leveraging the information contained in the sEMG gathered from multiple sources of activity. Natural, reliable and precise control of a dexterous hand prosthesis is a key ingredient to the restoration of a missing hand’s functions, to the best extent allowed for by the current technology. However, this kind of control, based upon machine learning applied to synergistic muscle activation patterns, is still not reliable enough to be used in the clinics. In this chapter we propose to use . machine learning to improve the stability and reliability of natural prosthetic control. Incremental learning enforces a true, endless adaptation of the prosthesis to the subject, the environment, the objects to be manipulated; and it allows for the adaptation of the subject to the prosthesis in the course of time, lea
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How to Map Human Hand Synergies onto Robotic Hands Using the SynGrasp Matlab Toolbox |
Gionata Salvietti,Guido Gioioso,Monica Malvezzi,Domenico Prattichizzo |
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Abstract
Throughout this book, we have described how neuroscientific findings on synergistic organization of human hand can be used to devise guidelines for the design and control of robotic and prosthetic hands as well as for sensing devices (see Chaps. ., ., . and .). However, the development of novel robotic devices open issues on how to generalize the outcomes to different architectures. In this chapter, we describe a mapping strategy to transfer human hand synergies onto robotic hands with dissimilar kinematics. The algorithm is based on the definition of two virtual objects that are used to abstract from the specific structures of the hands. The proposed mapping strategy allows to overcame the problems in defining synergies for robotic hands computing PCA analysis over a grasp dataset obtained empirically closing the robot hand upon different objects. The developed mapping framework has been implemented using the SynGrasp Matlab toolbox. This tool includes functions for the definition of hand kinematic structure and of the contact points with a grasped object, the coupling between joints induced by a synergistic control, compliance at the contact, joint and actuator levels. Its analys
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Quasi-Static Analysis of Synergistically Underactuated Robotic Hands in Grasping and Manipulation Ta |
Edoardo Farnioli,Marco Gabiccini,Antonio Bicchi |
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Abstract
As described in Chaps. .–., neuroscientific studies showed that the control of the human hand is mainly realized in a . way. Recently, taking inspiration from this observation, with the aim of facing the complications consequent to the high number of degrees of freedom, similar approaches have been used for the control of robotic hands. As Chap. . describes SynGrasp, a useful technical tool for grasp analysis of synergy-inspired hands, in this chapter recently developed analysis tools for studying robotic hands equipped with . underactuation (see Chap. .) are exhaustively described under a theoretical point of view. After a review of the quasi-static model of the system, the . (FGM) and its . (cFGM) are presented, from which it is possible to extract relevant information as, for example, the subspaces of the ., of the . and the .. The definitions of some relevant types of manipulation tasks (e.g. the ., realized maintaining the object configuration fixed but changing contact forces, or the ., in which the grasped object can be moved without modifying contact forces) are provided in terms of nullity or non-nullity of the variables describing the system. The feasibility of such prede
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A Simple Model of the Hand for the Analysis of Object Exploration |
Vonne van Polanen,Wouter M. Bergmann Tiest,Astrid M. L. Kappers |
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Abstract
When hand motions in haptic exploration are investigated, the measurement methods used might actually restrict the movements or the perception. The perception might be reduced because the skin is covered, e.g. with a data glove. Also, the range of possible motions might be limited, e.g. by wired sensors. Here, a model of the hand is proposed that is calculated from data obtained from a small number of sensors (6). The palmar side of the hand is not covered by sensors or tape, leaving the skin free for cutaneous perception. The hand is then modeled as 16 rigid 3D segments, with a hand palm and 5 individual fingers with 3 phalanges each. This model can be used for movement analysis in object exploration and contact point analysis. A validation experiment of an object manipulation task and a contact analysis showed good qualitative agreement of the model with the control measurements. The calculations, assumptions and limitations of the model are discussed in comparison with other methods.
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Synergy-Based Optimal Sensing Techniques for Hand Pose Reconstruction |
Matteo Bianchi,Paolo Salaris,Antonio Bicchi |
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Abstract
Most of the neuroscientific results on synergies and their technical implementations in robotic systems, which are widely discussed throughout this book (see e.g. Chaps. ., ., ., ., ., . and .), moved from the analysis of hand kinematics in free motion or during the interaction with the external environment. This observation motivates both the need for the development of suitable and manageable models for kinematic recordings, as described in Chap. ., and the calling for accurate and economic systems or “gloves” able to provide reliable hand pose reconstructions. However, this latter aspect, which represents a challenging point also for many human-machine applications, is hardly achievable in economically and ergonomically viable sensing gloves, which are often imprecise and limited. To overcome these limitations, in this chapter we propose to exploit the bi-directional relationship between neuroscience and robotic/artificial systems, showing how the findings achieved in one field can inspire and be used to advance the state of art in the other one, and vice versa. More specifically, our leading approach is to use the concept of kinematic synergies to optimally estimate the posture
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发表于 2025-3-21 18:57:31
