31,113 research outputs found
Autoencoding a Soft Touch to Learn Grasping from On-land to Underwater
Robots play a critical role as the physical agent of human operators in
exploring the ocean. However, it remains challenging to grasp objects reliably
while fully submerging under a highly pressurized aquatic environment with
little visible light, mainly due to the fluidic interference on the tactile
mechanics between the finger and object surfaces. This study investigates the
transferability of grasping knowledge from on-land to underwater via a
vision-based soft robotic finger that learns 6D forces and torques (FT) using a
Supervised Variational Autoencoder (SVAE). A high-framerate camera captures the
whole-body deformations while a soft robotic finger interacts with physical
objects on-land and underwater. Results show that the trained SVAE model
learned a series of latent representations of the soft mechanics transferrable
from land to water, presenting a superior adaptation to the changing
environments against commercial FT sensors. Soft, delicate, and reactive
grasping enabled by tactile intelligence enhances the gripper's underwater
interaction with improved reliability and robustness at a much-reduced cost,
paving the path for learning-based intelligent grasping to support fundamental
scientific discoveries in environmental and ocean research.Comment: 17 pages, 5 figures, 1 table, submitted to Advanced Intelligent
Systems for revie
Reach-To-Grasp Movements: A Multimodal Techniques Study
The aim of the present study was to investigate the correlation between corticospinal activity, kinematics, and electromyography (EMG) associated with the execution of precision and whole-hand grasps (WHGs). To this end, motor-evoked potentials (MEPs) induced by transcranial magnetic stimulation (TMS), EMG, and 3-D motion capture data have been simultaneously recorded during the planning and the execution of prehensile actions toward either a small or a large object. Differences in the considered measures were expected to distinguish between the two types of grasping actions both in terms of action preparation and execution. The results indicate that the index finger (FDI) and the little finger (ADM) muscles showed different activation patterns during grasping execution, but only the FDI appeared to distinguish between the two types of actions during motor preparation. Kinematics analysis showed that precision grips differed from WHGs in terms of displayed fingers distance when shaping before object\u2019s contact, and in terms of timing and velocity patterns. Moreover, significant correlations suggest a relationship between the muscular activation and the temporal aspects concerned with the index finger\u2019s extension during whole-hand actions. Overall, the present data seem to suggest a crucial role played by index finger as an early \u201cmarker\u201d of differential motor preparation for different types of grasps and as a \u201cnavigator\u201d in guiding whole-hand prehensile actions. Aside from the novelty of the methodological approach characterizing the present study, the data provide new insights regarding the level of crosstalk among different levels concerned with the neuro-behavioral organization of reach-to-grasp movements
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 objects position and orientation guides the hand to the object, while information about the objects 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 ones 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
Anthropomorphism Index of Mobility for Artificial Hands
The increasing development of anthropomorphic artificial hands makes necessary quick metrics that analyze their anthropomorphism. In this study, a human grasp experiment on the most important grasp types was undertaken in order to obtain an Anthropomorphism Index of Mobility (AIM) for artificial hands. The AIM evaluates the topology of the whole hand, joints and degrees of freedom (DoFs), and the possibility to control these DoFs independently. It uses a set of weighting factors, obtained from analysis of human grasping, depending on the relevance of the different groups of DoFs of the hand. The computation of the index is straightforward, making it a useful tool for analyzing new artificial hands in early stages of the design process and for grading human-likeness of existing artificial hands. Thirteen artificial hands, both prosthetic and robotic, were evaluated and compared using the AIM, highlighting the reasons behind their differences. The AIM was also compared with other indexes in the literature with more cumbersome computation, ranking equally different artificial hands. As the index was primarily proposed for prosthetic hands, normally used as nondominant hands in unilateral amputees, the grasp types selected for the human grasp experiment were the most relevant for the human nondominant hand to reinforce bimanual grasping in activities of daily living. However, it was shown that the effect of using the grasping information from the dominant hand is small, indicating that the index is also valid for evaluating the artificial hand as dominant and so being valid for bilateral amputees or robotic hands
Design method for an anthropomorphic hand able to gesture and grasp
This paper presents a numerical method to conceive and design the kinematic
model of an anthropomorphic robotic hand used for gesturing and grasping. In
literature, there are few numerical methods for the finger placement of
human-inspired robotic hands. In particular, there are no numerical methods,
for the thumb placement, that aim to improve the hand dexterity and grasping
capabilities by keeping the hand design close to the human one. While existing
models are usually the result of successive parameter adjustments, the proposed
method determines the fingers placements by mean of empirical tests. Moreover,
a surgery test and the workspace analysis of the whole hand are used to find
the best thumb position and orientation according to the hand kinematics and
structure. The result is validated through simulation where it is checked that
the hand looks well balanced and that it meets our constraints and needs. The
presented method provides a numerical tool which allows the easy computation of
finger and thumb geometries and base placements for a human-like dexterous
robotic hand.Comment: IEEE International Conference on Robotics and Automation, May 2015,
Seattle, United States. IEEE, 2015, Proceeding IEEE International Conference
on Robotics and Automatio
Grasping bulky objects with two anthropomorphic hands
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper presents an algorithm to compute precision grasps for bulky objects using two anthropomorphic hands. We use objects modeled as point clouds obtained from a sensor camera or from a CAD model. We then process the point clouds dividing them into two set of slices where we look for sets of triplets of points. Each triplet must accomplish some physical conditions based on the structure of the hands. Then, the triplets of points from each set of slices are evaluated to find a combination that satisfies the force closure condition (FC). Once one valid couple of triplets have been found the inverse kinematics of the system is computed in order to know if the corresponding points are reachable by the hands, if so, motion planning and a collision check are performed to asses if the final grasp configuration of the system is suitable. The paper
inclu des some application examples of the proposed approachAccepted versio
Port-Hamiltonian modeling for soft-finger manipulation
In this paper, we present a port-Hamiltonian model of a multi-fingered robotic hand, with soft-pads, while grasping and manipulating an object. The algebraic constraints of the interconnected systems are represented by a geometric object, called Dirac structure. This provides a powerful way to describe the non-contact to contact transition and contact viscoelasticity, by using the concepts of energy flows and power preserving interconnections. Using the port based model, an Intrinsically Passive Controller (IPC) is used to control the internal forces. Simulation results validate the model and demonstrate the effectiveness of the port-based approach
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