78 research outputs found
Experimental evaluation of synergy-based in-hand manipulation
In this paper, the problem of in-hand dexterous manipulation has been addressed on the base
of postural synergies analysis. The computation of the synergies subspace able to represent grasp and
manipulation tasks as trajectories connecting suitable configuration sets is based on the observation of
the human hand behavior. Five subjects are required to reproduce themost natural grasping configuration
belonging to the considered grasping taxonomy and the boundary configurations for those grasps that
admit internal manipulation. The measurements on the human hand and the reconstruction of the human
grasp configurations are obtained using a vision-based mapping method that assume the kinematics
of the robotic hand, used for the experiments, as a simplified model of the human hand. The analysis
to determine the most suitable set of synergies able to reproduce the selected grasps and the relative
allowed internal manipulation has been carried out. The grasping and in-hand manipulation tasks have
been reproduced bymeans of linear interpolation of the boundary configurations in the selected synergies
subspace and the results have been experimentally tested on the UB Hand IV
Intuitive Hand Teleoperation by Novice Operators Using a Continuous Teleoperation Subspace
Human-in-the-loop manipulation is useful in when autonomous grasping is not
able to deal sufficiently well with corner cases or cannot operate fast enough.
Using the teleoperator's hand as an input device can provide an intuitive
control method but requires mapping between pose spaces which may not be
similar. We propose a low-dimensional and continuous teleoperation subspace
which can be used as an intermediary for mapping between different hand pose
spaces. We present an algorithm to project between pose space and teleoperation
subspace. We use a non-anthropomorphic robot to experimentally prove that it is
possible for teleoperation subspaces to effectively and intuitively enable
teleoperation. In experiments, novice users completed pick and place tasks
significantly faster using teleoperation subspace mapping than they did using
state of the art teleoperation methods.Comment: ICRA 2018, 7 pages, 7 figures, 2 table
Planning hand-arm grasping motions with human-like appearance
© 2018 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 worksFinalista de l’IROS Best Application Paper Award a la 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, ICROS.This paper addresses the problem of obtaining human-like motions on hand-arm robotic systems performing pick-and-place actions. The focus is set on the coordinated movements of the robotic arm and the anthropomorphic mechanical hand, with which the arm is equipped. For this, human movements performing different grasps are captured and mapped to the robot in order to compute the human hand synergies. These synergies are used to reduce the complexity of the planning phase by reducing the dimension of the search space. In addition, the paper proposes a sampling-based planner, which guides the motion planning ollowing the synergies. The introduced approach is tested in an application example and thoroughly compared with other state-of-the-art planning algorithms, obtaining better results.Peer ReviewedAward-winningPostprint (author's final draft
Synergy-based policy improvement with path integrals for anthropomorphic hands
In this work, a synergy-based reinforcement learning
algorithm has been developed to confer autonomous grasping
capabilities to anthropomorphic hands. In the presence of
high degrees of freedom, classical machine learning techniques
require a number of iterations that increases with the size of the
problem, thus convergence of the solution is not ensured. The
use of postural synergies determines dimensionality reduction
of the search space and allows recent learning techniques, such
as Policy Improvement with Path Integrals, to become easily
applicable. A key point is the adoption of a suitable reward
function representing the goal of the task and ensuring onestep
performance evaluation. Force-closure quality of the grasp
in the synergies subspace has been chosen as a cost function
for performance evaluation. The experiments conducted on the
SCHUNK 5-Finger Hand demonstrate the effectiveness of the
algorithm showing skills comparable to human capabilities in
learning new grasps and in performing a wide variety from
power to high precision grasps of very small objects
A Control Architecture for Grasp Strength Regulation in Myocontrolled Robotic Hands Using Vibrotactile Feedback: Preliminary Results
Nowadays, electric-powered hand prostheses do not provide adequate sensory instrumentation and artificial feedback to allow users voluntarily and finely modulate the grasp strength applied to the objects. In this work, the design of a control architecture for a myocontrol-based regulation of the grasp strength for a robotic hand equipped with contact force sensors is presented. The goal of the study was to provide the user with the capability of modulating the grasping force according to target required levels by exploiting a vibrotactile feedback. In particular, the whole human-robot control system is concerned (i.e. myocontrol, robotic hand controller, vibrotactile feedback.) In order to evaluate the intuitiveness and force tracking performance provided by the proposed control architecture, an experiment was carried out involving four naïve able-bodied subjects in a grasping strength regulation task with a myocontrolled robotic hand (the University of Bologna Hand), requiring for grasping different objects with specific target force levels. The reported results show that the control architecture successfully allowed all subjects to achieve all grasping strength levels exploiting the vibrotactile feedback information. This preliminary demonstrates that, potentially, the proposed control interface can be profitably exploited in upper-limb prosthetic applications, as well as for non-rehabilitation uses, e.g. in ultra-light teleoperation for grasping devices
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
Mechanical implementation of kinematic synergy for continual grasping generation of anthropomorphic hand
The synergy-based motion generation of current anthropomorphic hands generally employ the static posture synergy, which is extracted from quantities of joint trajectory, to design the mechanism or control strategy. Under this framework, the temporal weight sequences of each synergy from pregrasp phase to grasp phase are required for reproducing any grasping task. Moreover, the zero-offset posture has to be preset before starting any grasp. Thus, the whole grasp phase appears to be unlike natural human grasp. Up until now, no work in the literature addresses these issues toward simplifying the continual grasp by only inputting the grasp pattern. In this paper, the kinematic synergies observed in angular velocity profile are employed to design the motion generation mechanism. The kinematic synergy extracted from quantities of grasp tasks is implemented by the proposed eigen cam group in tendon space. The completely continual grasp from the fully extending posture only require averagely rotating the two eigen cam groups one cycle. The change of grasp pattern only depends on respecifying transmission ratio pair for the two eigen cam groups. An illustrated hand prototype is developed based on the proposed design principle and the grasping experiments demonstrate the feasibility of the design method. The potential applications include the prosthetic hand that is controlled by the classified pattern from the bio-signal
Leveraging Kernelized Synergies on Shared Subspace for Precision Grasping and Dexterous Manipulation
Manipulation in contrast to grasping is a trajectorial task that needs to use dexterous hands. Improving the dexterity of robot hands, increases the controller complexity and thus requires to use the concept of postural synergies. Inspired from postural synergies, this research proposes a new framework called kernelized synergies that focuses on the re-usability of same subspace for precision grasping and dexterous manipulation. In this work, the computed subspace of postural synergies is parameterized by kernelized movement primitives to preserve its grasping and manipulation characteristics and allows its reuse for new objects. The grasp stability of proposed framework is assessed with the force closure quality index, as a cost function. For performance evaluation, the proposed framework is initially tested on two different simulated robot hand models using the Syngrasp toolbox and experimentally, four complex grasping and manipulation tasks are performed and reported. Results confirm the hand agnostic approach of proposed framework and its generalization to distinct objects irrespective of their dimensions
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