Grasping

Questo lavoro propone un nuovo strumento psicometrico per diagnosticare l’aprassia ideomotoria. A partire da modelli cognitivi che assumono l’esistenza di due processi cognitivi per l’imitazione di gesti, distinti funzionalmente e anatomicamente (una via semantica per l’imitazione dei gesti conosciuti e una via diretta
per la riproduzione di gesti nuovi), il test mira a identificare deficit selettivi in base al contenuto (gesti conosciuti vs gesti nuovi) e al segmento dell’arto superiore che esegue il movimento (distale vs prossimale). Diagnosticare in modo adeguato il disturbo aprassico ideomotorio è infatti una condizione necessaria per programmare l’iter riabilitativo dei pazienti con lesioni cerebrovascolari.

In this work, efficient design methods for robotic manipulators are initially investigated. Successively, the possibility of developing a flexible control architecture that allows for controlling different manipulators by using a universal input device is outlined. The main challenge of doing this consists of finding a flexible way to map the normally fixed DOFs of the input controller to the variable DOFs of the specific manipulator to be controlled. This process has to be realised regardless of the differences in size, kinematic structure, body morphology, constraints and affordances. Different alternative control algorithms are investigated including effective approaches that do not assume a priori knowledge for the Inverse Kinematic (IK) models. These algorithms derive the kinematic properties from biologically-inspired approaches, machine learning procedures or optimisation methods. In this way, the system is able to automatically learn the kinematic properties of different manipulators. Finally, a methodology for performing experimental activities in the area of maritime cranes and robotic arm control is outlined. By combining the rapid-prototyping approach with the concept of interchangeable interfaces, a simulation and benchmarking framework for advanced control methods of maritime cranes and robotic arms is presented.

This thesis presents the design, modeling, and development of adaptive robot hands that are capable of performing dexterous, in-hand manipulation. The robot hand comprises of anthropomorphic robotic fingers, which employ an adaptive actuation mechanism. The mechanism achieves both flexion/extension and adduction/abduction, on the finger’s metacarpophalangeal joint, by using two actuators. Moment arm pulleys are employed to drive the tendon laterally, such that an amplification on the abduction motion occurs, while also maintaining the flexion motion. Particular emphasis has been given to the modeling and the analysis of the actuation mechanism. Also, a model for spatial motion is provided that relates the actuation modes with the finger motion and the tendon force with the finger characteristics. For the hand design, the use of differential mechanisms simplifies the actuation scheme, as we utilize only two actuators for four fingers, achieving affordable dexterity. A design optimization framework assess the results of hand anthropometry studies to derive key parameters for the bio-inspired actuation design. The model assumptions are evaluated with the finite element method. The proposed finger has been fabricated with the Hybrid Deposition Manufacturing technique and the actuation mechanism’s efficiency has been validated with experiments that include the computation of the finger workspace, the assessment of the force exertion capabilities, the demonstration of the feasible motions, and the grasping and manipulation capabilities. Also, the hand design is fabricated with off-the-shelf materials and rapid prototyping techniques while its efficiency has been validated using an extensive set of experimental paradigms that involved the execution of grasping and in-hand manipulation tasks with everyday objects.

The paper argues that an account of understanding should take the form of a Carnapian explication and acknowledge that understanding comes in degrees. An explication of objectual understanding is defended, which helps to make sense of the cognitive achievements and goals of science. The explication combines a necessary condition with three evaluative dimensions: An epistemic agent understands a subject matter by means of a theory only if the agent commits herself sufficiently to the theory of the subject matter, and to the degree that the agent grasps the theory (i.e., is able to make use of it), the theory answers to the facts and the agent's commitment to the theory is justified. The threshold for outright attributions of understanding is determined contextually. The explication has descriptive as well as normative facets and allows for the possibility of understanding by means of non-explanatory (e.g., purely classificatory) theories.

Motor activations reported during action-word understanding have raised the question as to whether the system for motor production contains semantically-relevant information. Cognitive neuropsychologists have provided compelling evidence that damage to the system for production of object-directed (transitive) actions does not necessarily lead to detrimental changes in the individuals' ability to understand the corresponding action words, and vice versa. We addressed this question focusing on intransitive symbolic gestures (emblems; e.g., waving goodbye), which are known to engage different resources, or neural representations, than object-directed actions, and are thought to enjoy a special relationship with language, due to a lexicalized relation between form (the gesture) and its meaning. We tested 12 left-damaged patients (and 17 healthy controls) on praxis (imitation and gesturing-to-verbal-command) and lexical-semantic tasks (naming and word-picture matching) involving the same emblems. With the group-level analyses, we replicated correlations between praxis and language deficits typically observed in left-damaged patients. The analyses of patients' performance at the single-case level, however, revealed double dissociations between the ability to produce emblems and the ability to retrieve and recognize their lexical-semantic definition. Double dissociations, even in the event of positive group-level correlations across tasks, imply that the motor representation of a gesture and the lexical-semantic representation of the corresponding word rely on functionally independent system. This study is the first systematic neuropsychological investigation of the relationship between the lexical-semantic and the motor representation of emblems, the closest counterpart of words in the gestural domain.Copyright © 2012 Elsevier Srl. All rights reserved.

Although recently developed Electromyographybased (EMG) prosthetic hands could classify a significant amount of wrist motions, classifying 5-6 grasping patterns in real-time is a challenging task. The collaboration of EMG and vision has addressed this problem to a certain extent but could not achieve significant performance in real-time. In this paper, we propose a fusion method that can improve the real-time prediction accuracy of the EMG system by merging a probability matrix that represents the usage of the six grasping patterns for the targeted object. The YOLO object detection system retrieves a probability matrix of the identified object, and it is used to correct the error in the EMG classification system. The experiments revealed that the optimized ANN model outperformed the KNN, LDA, NB, and DT by achieving the highest mean True Positive Rate (mTPR) of 69.34%(21.54) in real-time for all the six grasping patterns. Furthermore, the proposed feature set (Age, Gender, and Handedness of the user) showed that their influence increases the mTPR of ANN by 16.05%(2.70). The proposed system takes 393.89ms(178.23ms) to produce a prediction. Therefore, the user does not feel a delay between intention and execution. Furthermore, the system facilitates users to use multiple-grasping patterns for an object.

In this paper, we present a methodology based on constrained optimization methods for estimating and compensating for post-contact parasitic object motions for underactuated compliant robot hands and for deriving stable, minimal effort grasps to try to minimize those movements. To do so, we compute the object motions for different hand designs, object shapes, and object sizes, and we synthesize appropriate robot arm trajectories that eliminate them, even in hands with complex flexure-based compliant members. The effectiveness of the proposed methods is validated using a seven DOF robot arm (Barrett WAM) and a range of compliant, underactuated robot hands (Yale Open Hand models T42PP, T42PF and T42FF).

Abstract— In this article a robotic gripper with the ability of cylindrical object grasping is designed and fabricated. With the increasing use of robotic arms in industry, grasping and holding, as a part of industrial processes, are of great importance. Hence, proper design of grippers plays a key role in efficient performance of robotic arms. The main feature of this design is its reliability and efficiency while maintaining simplicity. This robotic gripper is designed for installing on a mobile robot with the task of holding and moving cylindrical object. One of the aspects of an efficient robotic gripper is its ability to satisfy the “Form-gripping” feature which is utilized for the presented design. Kinematic consideration is examined by Catia. Finally, control methods through Matlab and experimental test results are presented in this paper.

This thesis focuses on the design and development of an anthropomorphic, underactuated, personalized robot hand of low cost and weight.

Human hand is the most versatile and dexterous end–effector known, so in this thesis we argue that anthropomorphism is an important feature for hand design. Elastic joints were selected in order to introduce passive compliance in the hand structure, simplify the control problem and perform robust grasping in unstructured environments. Low weight and cost were achieved with the use of common, lightweight and off–the–self materials, like Plexiglas & ABS. Moreover, in this thesis we select to use a single actuator with a novel selectively differential mechanism that allows the execution of different grasping postures and gestures.

The aforementioned specifcations led us to appropriate design choices for the anthropomorphic robot hand. More precisely, the Kapandji test was used to derive the exact positions of thumb joints, a toothed locking mechanism was implemented for the opposition of the thumb and a novel selectively lockable differential mechanism, based on the Whifetree, was developed. The proposed design is parametric and allows the replication of personalized, anthropomorphic robotic and prosthetic hands. The proposed hands can be manufactured with either a laser cutter or a 3D printer.

In order to validate the efciency of the proposed robot hand design we conducted two different sets of experiments. During the frst set, the efciency of the selectively lockable differential mechanism was assessed, while the second set assessed the ability of the hand to efciently grasp a wide range of everyday life objects. The suggested robot hand can be used for a variety of robotic applications as well as the creation of an affordable, light–weight prosthesis.

Object manipulation is a challenging task for robotics, as the physics involved in object interaction is com- plex and hard to express analytically. Here we introduce a modular approach for learning a manipulation strategy from human demonstration. Firstly we record a human perform- ing a task that requires an adaptive control strategy in differ- ent conditions, i.e. different task contexts. We then perform modular decomposition of the control strategy, using phases of the recorded actions to guide segmentation. Each mod- ule represents a part of the strategy, encoded as a pair of forward and inverse models. All modules contribute to the final control policy; their recommendations are integrated via a system of weighting based on their own estimated er- ror in the current task context. We validate our approach by demonstrating it, both in a simulation for clarity, and on a real robot platform to demonstrate robustness and capacity to generalise. The robot task is opening bottle caps. We show that our approach can modularize an adaptive control strategy and generate appropriate motor commands for the robot to accomplish the complete task, even for novel bottles.

Perceptual estimates of three-dimensional (3D) properties, such as the distance and depth of an object, are often inaccurate. Given the accuracy and ease with which we pick up objects, it may be expected that perceptual distortions do not affect how the brain processes 3D information for reach-to-grasp movements. Nonetheless, empirical results show that grasping accuracy is reduced when visual feedback of the hand is removed. Here we studied whether specific types of training could correct grasping behavior to perform adequately even when any form of feedback is absent. Using a block design paradigm, we recorded the movement kinematics of subjects grasping virtual objects located at different distances in the absence of visual feedback of the hand and haptic feedback of the object, before and after different training blocks with different feedback combinations (vision of the thumb and vision of thumb and index finger, with and without tactile feedback of the object). In the Pretraining block, we found systematic biases of the terminal hand position, the final grip aperture, and the maximum grip aperture like those reported in perceptual tasks. Importantly, the distance at which the object was presented modulated all these biases. In the Posttraining blocks only the hand position was partially adjusted, but final and maximum grip apertures remained unchanged. These findings show that when visual and haptic feedback are absent systematic distortions of 3D estimates affect reach-to-grasp movements in the same way as they affect perceptual estimates. Most importantly, accuracy cannot be learned, even after extensive training with feedback.

Lifting objects, whose mass may produce high wrist torques that exceed the hardware strength limits, could lead to unstable grasps or serious robot damage. This work introduces a new Center-of-Mass (CoM)-based grasp pose adaptation method, for picking up objects using a combination of exteroceptive 3D perception and proprioceptive force/torque sensor feedback. The method works in two iterative stages to provide reliable and wrist torque efficient grasps. Initially, a geometric object CoM is estimated from the input range data. In the first stage, a set of hand-size handle grasps are localized on the object and the closest to its CoM is selected for grasping. In the second stage, the object is lifted using a single arm, while the force and torque readings from the sensor on the wrist are monitored. Based on these readings, a displacement to the new CoM estimation is calculated. The object is released and the process is repeated until the wrist torque effort is minimized. The advantage of our method is the blending of both exteroceptive (3D range) and proprioceptive (force/torque) sensing for finding the grasp location that minimizes the wrist effort, potentially improving the reliability of the grasping and the subsequent manipulation task. We experimentally validate the proposed method by executing a number of tests on a set of objects that include handles, using the humanoid robot WALK-MAN.

It is generally accepted that vision first evolved for the distal control of movement and that perception or 'representational' vision emerged much later. Vision-for-action operates in real time and uses egocentric frames of reference and the real metrics of the world. Vision-for-perception can operate over longer time scales and is much more scene-based in its computations. These differences in the timing and metrics of the two systems have been examined in experiments that have looked at the way in which each system deals with visual illusions. Although controversial, the consensus is that actions such as grasping and reaching are often unaffected by high-level pictorial illusions, which by definition affect perception. However, recent experiments have shown that, for actions to escape the effects of such illusions, they must be highly practiced actions, preferably with the right hand, and must be directed in real time at visible targets. This latter finding suggests that some of the critical components of the encapsulated (bottom-up) systems that mediate the visual control of skilled reaching and grasping movements are lateralised to the left hemisphere.

ABSRACT Many of the robotic grasping researches have been focusing on stationary objects. And for dynamic moving objects, researchers have been using real time captured images to locate objects dynamically. However, this approach of controlling the grasping process is quite costly, implying a lot of resources and image processing.Therefore, it is indispensable to seek other method of simpler handling… In this paper, we are going to detail the requirements to manipulate a humanoid robot arm with 7 degree-of-freedom to grasp and handle any moving objects in the 3-D environment in presence or not of obstacles and without using the cameras. We use the OpenRAVE simulation environment, as well as, a robot arm instrumented with the Barrett hand. We also describe a randomized planning algorithm capable of planning. This algorithm is an extent of RRT-JT that combines exploration, using a Rapidly-exploring Random Tree, with exploitation, using Jacobian-based gradient descent, to instruct a 7-DoF WAM robotic arm, in order to grasp a moving target, while avoiding possible encountered obstacles. We present a simulation of a scenario that starts with tracking a moving mug then grasping it and finally placing the mug in a determined position, assuring a maximum rate of success in a reasonable time.

Grasping is one of a few adaptive mechanisms that, in conjunction with clinging, hooking, arm swinging, adhering, and flying, allowed for incursion into the arboreal eco-space. Little research has been done that addresses grasping as an enhanced manual ability in non-mammalian tetrapods, with the exception of studies comparing the anatomy of muscle and tendon structure. Previous studies showed that grasping abilities allow exploitation for narrow branch habitats and that this adaptation has clear osteological consequences. The objective of this work is to ascertain the existence of morphometric descriptors in the hand skeleton of lizards related to grasping functionality. A morphological matrix was constructed using 51 morphometric variables in 278 specimens, from 24 genera and 13 families of Squamata. To reduce the dimensions of the dataset and to organize the original variables into a simpler system, three PCAs (Principal Component Analyses) were performed using the subsets of (1) carpal variables, (2) metacarpal variables, and (3) phalanges variables. The variables that demonstrated the most significant contributions to the construction of the PCA synthetic variables were then used in subsequent analyses. To explore which morphological variables better explain the variations in the functional setting, we ran Generalized Linear Models for the three different sets. This method allows us to model the morphology that enables a particular functional trait. Grasping was considered the only response variable, taking the value of 0 or 1, while the original variables retained by the PCAs were considered predictor variables. Our analyses yielded six variables associated with grasping abilities: two belong to the carpal bones, two belong to the metacarpals and two belong to the phalanges. Grasping in lizards can be performed with hands exhibiting at least two different independently originated combinations of bones. The first is a combination of a highly elongated centrale bone, reduced palmar sesamoid, divergence angles above 90 • , and slender metacarpal V and phalanges, such as exhibited by Anolis sp. and Tropidurus sp. The second includes an elongated centrale bone, lack of a palmar sesamoid, divergence angles above 90 • , and narrow metacarpal V and phalanges, as exhibited by geckos. Our data suggest that the morphological distinction between graspers and non-graspers is demonstrating the existence of ranges along the morphological continuum within which a new ability is generated. Our results support the hypothesis of the nested origin of grasping abilities within arboreality. Thus, the manifestation of grasping abilities as a response to locomotive selective pressure in the context of narrow-branch eco-spaces could also enable other grasping-dependent biological roles, such as prey handling.

Many theories have been put forward to explain the origins of right-handedness in humans. Here we present evidence that
this preference may stem in part from a right hand advantage in grasping for feeding. Thirteen participants were asked to
reach-to-grasp food items of 3 different sizes: SMALL (Cheerios), MEDIUM (Froot Loops), and LARGE (Oatmeal Squares). Participants used both their right- and left-hands in separate blocks (50 trials each, starting order counterbalanced) to grasp
the items. After each grasp, participants either a) ate the food item, or b) placed it inside a bib worn beneath his/her chin (25
trials each, blocked design, counterbalanced). The conditions were designed such that the outward and inward movement
trajectories were similar, differing only in the final step of placing it in the mouth or bib. Participants wore Plato liquid crystal goggles that blocked vision between trials. All trials were conducted in closed-loop with 5000 ms of vision. Hand kinematics were recorded by an Optotrak Certus, which tracked the position of three infrared diodes attached separately to the index finger, thumb, and wrist. We found a task (EAT/PLACE) by hand (LEFT/RIGHT) interaction on maximum grip aperture (MGA; the maximum distance between the index finger and thumb achieved during grasp pre-shaping). MGAs were smaller during right-handed movements, but only when grasping with intent to eat. Follow-up tests show that the RIGHT-HAND/EAT MGA was significantly smaller than all other hand/task conditions. Because smaller grip apertures are typically associated with greater precision, our results demonstrate a right-hand advantage for the grasp-to-eat movement. From an evolutionary perspective, early humans may have preferred the hand that could grasp food with more precision, thereby maximizing the likelihood of retrieval, consumption, and consequently, survival.

In order for robots to expand their applicability within manufacturing operations, their dexterity needs to advance considerably. There is active research in many areas that is contributing to creating a new generation of much more dexterous arms and hands. A complementary measurement science research effort, which defines the metrics, artifacts, and test methods, is needed to characterize and understand performance of new capabilities. In this document, a high-level roadmap is proposed for potential long- and short-term work to progress measurement science in the area of dexterous manipulation.

Previous developmental research suggests that motor experience supports the development of action perception across the lifespan. However, it is still unknown when the neural mechanisms underlying action-perception coupling emerge in infancy. The goal of this study was to examine the neural correlates of action perception during the emergence of grasping abilities in newborn rhesus macaques. Neural activity, recorded via electro-encephalogram (EEG), while monkeys observed grasping actions, mimed actions and means-end movements during the first (W1) and second week (W2) of life was measured. Event-related desynchronization (ERD) during action observation was computed from the EEG in the alpha and beta bands, two components of the sensor-imotor mu rhythm associated with activity of the mirror neuron system (MNS). Results revealed age-related changes in the beta band, but not the alpha band, over anterior electrodes, with greater desynchronization at W2 than W1 for the observation of grasping actions. Additionally, desynchronization to observed grasping actions at W2 was associated with infants' motor skills-measured by a separate behavioral task-such that more grasping attempts were associated to greater beta ERD. These findings suggest the emergence of an early action-perception system, that relies on motor experience, shortly after birth.