A new view on grasping

Reaching out for an object is often described as consisting of two components that are based on different visual information. Information about the object’s position and orientation guides the hand to the object, while information about the object’s shape and size determines how the fingers move relative to the thumb to grasp it. We propose an alternative description, which consists of determining suitable positions on the object — on the basis of its shape, surface roughness, and so on — and then moving one’s thumb and fingers more or less independently to these positions. We modelled this description using a minimum jerk approach, whereby the finger and thumb approach their respective target positions approximately orthogonally to the surface. Our model predicts how experimental variables such as object size, movement speed, fragility, and required accuracy will influence the timing and size of the maximum aperture of the hand. An extensive review of experimental studies on grasping showed that the predicted influences correspond to human behaviour

A Neural Circuit for Coordinating Reaching with Grasping: Autocompensating Variable Initial Apertures, Perturbations to Target Size, and Perturbations to Target Orientation

A neural network model is presented, that extends principles of the VITE (vector integration to end-point) model [1, 2, 3, 4] of primate reaching to the more complex case of reach-grasp coordination. The main new planning problem addressed by the model is how to simulate human data on temporal coordination between reaching and grasping, while at the same time remaining stable and compensating for altered initial apertures and perturbations of object size and object location/ orientation. Simulations of the model replicate key features of four different experimental protocols with a single set of parameters. The proposed circuit computes reaching to grasp trajectories in real-time, by continuously updating vector positioning commands, and with no precomputation of total or component movement times. The model consists of three generator channels: transport, which generates a reaching trajectory; aperture, which controls distance between thumb and index finger; and orientation, which controls hand orientation vis-a-vis target's orientation.CONACYT of Mexico; Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409

A Neural Circuit for Coordinating Reaching with Grasping: Autocompensating Variable Initial Apertures, Perturbations to Target Size, and Perturbations to Target Orientation

A neural network model is presented, that extends principles of the VITE (vector integration to end-point) model [1, 2, 3, 4] of primate reaching to the more complex case of reach-grasp coordination. The main new planning problem addressed by the model is how to simulate human data on temporal coordination between reaching and grasping, while at the same time remaining stable and compensating for altered initial apertures and perturbations of object size and object location/ orientation. Simulations of the model replicate key features of four different experimental protocols with a single set of parameters. The proposed circuit computes reaching to grasp trajectories in real-time, by continuously updating vector positioning commands, and with no precomputation of total or component movement times. The model consists of three generator channels: transport, which generates a reaching trajectory; aperture, which controls distance between thumb and index finger; and orientation, which controls hand orientation vis-a-vis target's orientation.CONACYT of Mexico; Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409

Orientation of the opposition axis in mentally simulated grasping

Five normal subjects were tested in a simulated grasping task. A cylindrical container filled with water was placed on the center of a horizontal monitor screen. Subjects used a precision grip formed by the thumb and index finger of their right hand. After a preliminary run during which the container was present, it was replaced by an image of the upper surface of the cylinder appearing on the horizontal computer screen on which the real cylinder was placed during the preliminary run. In each trial the image was marked with two contact points which defined an opposition axis in various orientations with respect to the frontal plane. The subjects’ task consisted, once shown a stimulus, of judging as quickly as possible whether the previously experienced action of grasping the container full of water and pouring the water out would be easy, difficult or impossible with the fingers placed according to the opposition axis indicated on the circle. Response times were found to be longer for the grasps judged to be more difficult due to the orientation and position of the opposition axis. In a control experiment, three subjects actually performed the grasps with different orientations and positions of the opposition axis. The effects of these parameters on response time followed the same trends as during simulated movements. This result shows that simulated hand movements take into account the same biomechanical limitations as actually performed movements

Redundant sensorized arm+hand system for space telerobotized manipulation

An integrated system, composed of an arm, a wrist, and a mechanical multifingered hand is treated. The hand is on development for possible application in telemanipulation, and is realized in separate parts. The redundancy of the degrees of freedom of the system, the sensors, the application of logical rules, and the supervision of teleoperators may be applied in order to have an optimum of reliability of the system in space telemanipulations

Design and control of a multi-fingered robot hand provided with tactile feedback

The design, construction, control and application of a three fingered robot hand with nine degrees of freedom and built-in multi-component force sensors is described. The adopted gripper kinematics are justified and optimized with respect to grasping and manipulation flexibility. The hand was constructed with miniature motor drive systems imbedded into the fingers. The control is hierarchically structured and is implemented on a simple PC-AT computer. The hand's dexterity and intelligence are demonstrated with some experiments

A review and consideration on the kinematics of reach-to-grasp movements in macaque monkeys

The bases for understanding the neuronal mechanisms that underlie the control of reach-to-grasp movements among nonhuman primates, particularly macaques, has been widely studied. However, only a few kinematic descriptions of their prehensile actions are available. A thorough understanding of macaques' prehensile movements is manifestly critical, in light of their role in biomedical research as valuable models for studying neuromotor disorders and brain mechanisms, as well as for developing brain-machine interfaces to facilitate arm control. This article aims to review the current state of knowledge on the kinematics of grasping movements that macaques perform in naturalistic, semi-naturalistic, and laboratory settings, to answer the following questions: Are kinematic signatures affected by the context within which the movement is performed? In what ways is kinematics of humans' and macaques' prehensile actions similar/dissimilar? Our analysis reflects the challenges involved in making comparisons across settings and species due to the heterogeneous picture in terms of the number of subjects, stimuli, conditions, and hands used. The kinematics of free-ranging macaques are characterized by distinctive features that are exhibited neither by macaques in laboratory setting nor human subjects. The temporal incidence of key kinematic landmarks diverges significantly between species, indicating disparities in the overall organization of movement. Given such complexities, we attempt a synthesis of extant body of evidence, intending to generate some significant implications for directions that future research might take, to recognize the remaining gaps and pursue the insights and resolutions to generate an interpretation of movement kinematics that accounts for all settings and subjects

Tele-operated high speed anthropomorphic dextrous hands with object shape and texture identification

This paper reports on the development of two number of robotic hands have been developed which focus on tele-operated high speed anthropomorphic dextrous robotic hands. The aim of developing these hands was to achieve a system that seamlessly interfaced between humans and robots. To provide sensory feedback, to a remote operator tactile sensors were developed to be mounted on the robotic hands. Two systems were developed, the first, being a skin sensor capable of shape reconstruction placed on the palm of the hand to feed back the shape of objects grasped and the second is a highly sensitive tactile array for surface texture identification

Seven properties of self-organization in the human brain

The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward