1,713 research outputs found
Global optimization of robotic grasps
This paper presents a procedure to optimize the quality of robotic grasps for objects that need to be held and manipulated in a specific way, characterized by a number of tight contact constraints. The main difficulties of the problem include
that the set of feasible grasps is a manifold implicitly defined by a system of non-linear equations, the high dimension of this manifold, and the multi-modal nature of typical grasp quality indices, which make local optimization methods get trapped into
local extrema. The proposed procedure finds a way around these difficulties by focussing the exploration on a relevant subset of grasps of lower dimension, which is traced out exhaustively using higher-dimensional continuation techniques. Using these
techniques, a detailed atlas of the subset is obtained, on which the highest quality grasp according to any desired criterion can
be readily identified. Experiments on a 3-finger planar hand and on the Schunk anthropomorphic hand validate the approach.Peer ReviewedPostprint (author’s final draft
A stiffness-based quality measure for compliant grasps and fixtures
This paper presents a systematic approach to quantifying the effectiveness of compliant grasps and fixtures of an object. The approach is physically motivated and applies to the grasping of two- and three-dimensional objects by any number of fingers. The approach is based on a characterization of the frame-invariant features of a grasp or fixture stiffness matrix. In particular, we define a set of frame-invariant characteristic stiffness parameters, and provide physical and geometric interpretation for these parameters. Using a physically meaningful scheme to make the rotational and translational stiffness parameters comparable, we define a frame-invariant quality measure, which we call the stiffness quality measure. An example of a frictional grasp illustrates the effectiveness of the quality measure. We then consider the optimal grasping of frictionless polygonal objects by three and four fingers. Such frictionless grasps are useful in high-load fixturing applications, and their relative simplicity allows an efficient computation of the globally optimal finger arrangement. We compute the optimal finger arrangement in several examples, and use these examples to discuss properties that characterize the stiffness quality measure
Unscented Bayesian Optimization for Safe Robot Grasping
We address the robot grasp optimization problem of unknown objects
considering uncertainty in the input space. Grasping unknown objects can be
achieved by using a trial and error exploration strategy. Bayesian optimization
is a sample efficient optimization algorithm that is especially suitable for
this setups as it actively reduces the number of trials for learning about the
function to optimize. In fact, this active object exploration is the same
strategy that infants do to learn optimal grasps. One problem that arises while
learning grasping policies is that some configurations of grasp parameters may
be very sensitive to error in the relative pose between the object and robot
end-effector. We call these configurations unsafe because small errors during
grasp execution may turn good grasps into bad grasps. Therefore, to reduce the
risk of grasp failure, grasps should be planned in safe areas. We propose a new
algorithm, Unscented Bayesian optimization that is able to perform sample
efficient optimization while taking into consideration input noise to find safe
optima. The contribution of Unscented Bayesian optimization is twofold as if
provides a new decision process that drives exploration to safe regions and a
new selection procedure that chooses the optimal in terms of its safety without
extra analysis or computational cost. Both contributions are rooted on the
strong theory behind the unscented transformation, a popular nonlinear
approximation method. We show its advantages with respect to the classical
Bayesian optimization both in synthetic problems and in realistic robot grasp
simulations. The results highlights that our method achieves optimal and robust
grasping policies after few trials while the selected grasps remain in safe
regions.Comment: conference pape
Data-Driven Grasp Synthesis - A Survey
We review the work on data-driven grasp synthesis and the methodologies for
sampling and ranking candidate grasps. We divide the approaches into three
groups based on whether they synthesize grasps for known, familiar or unknown
objects. This structure allows us to identify common object representations and
perceptual processes that facilitate the employed data-driven grasp synthesis
technique. In the case of known objects, we concentrate on the approaches that
are based on object recognition and pose estimation. In the case of familiar
objects, the techniques use some form of a similarity matching to a set of
previously encountered objects. Finally for the approaches dealing with unknown
objects, the core part is the extraction of specific features that are
indicative of good grasps. Our survey provides an overview of the different
methodologies and discusses open problems in the area of robot grasping. We
also draw a parallel to the classical approaches that rely on analytic
formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic
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