17 research outputs found
Learning Grasps in a Synergy-based Framework
In this work, a supervised learning strategy has been applied in conjunction with a control strategy to provide anthropomorphic hand-arm systems with autonomous grasping capabilities. Both learning and control algorithms have been developed in a synergy-basedframework in order to address issues related to high dimension of the configuration space, that typically characterizes robotic hands and arms with humanlike kinematics. An experimental setup has been built to learn hand-arm motion from humans during reaching and grasping tasks. Then, a Neural Network (NN) has been realized to generalize the grasps learned by imitation. Since the NN approximates the relationship between the object characteristics and the grasp configuration of the hand-arm system, a synergy-based control strategy has been applied to overcome planning errors. The reach-to-grasp strategy has been tested on a setup constituted by the KUKA LWR 4+Arm and the SCHUNK 5-Finger Hand
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
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
FEM-based Deformation Control for Dexterous Manipulation of 3D Soft Objects
International audienceIn this paper, a method for dexterous manipulation of 3D soft objects for real-time deformation control is presented, relying on Finite Element modelling. The goal is to generate proper forces on the fingertips of an anthropomor-phic device during in-hand manipulation to produce desired displacements of selected control points on the object. The desired motions of the fingers are computed in real-time as an inverse solution of a Finite Element Method (FEM), the forces applied by the fingertips at the contact points being modelled by Lagrange multipliers. The elasticity parameters of the model are preliminarly estimated using a vision system and a force sensor. Experimental results are shown with an underactuated anthropomorphic hand that performs a manipulation task on a soft cylindrical object
Calibration of tactile/force sensors for grasping with the PRISMA Hand II
The PRISMA Hand II is a mechanically robust anthropomorphic hand developed at PRISMA Lab, University of Naples Federico II. The hand is highly underactuated, three motors drive 19 joints via elastic tendons. Thanks to its particular mechanical design, the hand can perform not only adaptive grasps but also in-hand manipulation. Each fingertip integrates a tactile/force sensor, based on optoelectronic technology, to provide tactile/force feedback during grasping and manipulation, particularly useful with deformable objects. The paper briefly describes the mechanical design and sensor technology of the hand and proposes a calibration procedure for tactile/force sensors. A comparison between different models of Neural Networks architectures, suitable for sensors calibration, is shown. Experimental tests are provided to choose the optimal tactile sensing suite. Finally, experiments for the regulation of the forces are made to show the effectiveness of calibrated sensors
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
Vision Based Adaptation to Kernelized Synergies for Human Inspired Robotic Manipulation
Humans in contrast to robots are excellent in performing fine manipulation
tasks owing to their remarkable dexterity and sensorimotor organization.
Enabling robots to acquire such capabilities, necessitates a framework that not
only replicates the human behaviour but also integrates the multi-sensory
information for autonomous object interaction. To address such limitations,
this research proposes to augment the previously developed kernelized synergies
framework with visual perception to automatically adapt to the unknown objects.
The kernelized synergies, inspired from humans, retain the same reduced
subspace for object grasping and manipulation. To detect object in the scene, a
simplified perception pipeline is used that leverages the RANSAC algorithm with
Euclidean clustering and SVM for object segmentation and recognition
respectively. Further, the comparative analysis of kernelized synergies with
other state of art approaches is made to confirm their flexibility and
effectiveness on the robotic manipulation tasks. The experiments conducted on
the robot hand confirm the robustness of modified kernelized synergies
framework against the uncertainties related to the perception of environment