In general, automatic grasp synthesis can be thought of the task of finding the combination of hand posture (intrinsic degrees of freedom, or DOF’s) and position (extrinsic DOF’s) that produces a stable grasp, according to a given grasp quality metric. From this perspective, it can be approached as an optimization problem, seeking to maximize the value of the grasp quality Q expressed as a function over a highdimensional domain: Q = f(p, w) (1) If d is the number of intrinsic hand DOF’s, p ∈ R d represents the hand posture and w ∈ R 6 contains the position and orientation of the wrist. The traditional form for specifying a hand posture is to set a value for each individual DOF of the hand. For

This paper presents an incremental and scalable learning algorithm in order to mine numeric, low dimensionality, high--cardinality, time--changing data streams. Within the Supervised Learning field, our approach, named SCALLOP, provides a set of decision rules whose size is very near

Abstract. We want to publish low-dimensional points, for example 2D spatial points, in a differentially private manner. Most existing mechanisms publish noisy frequency counts of points in a fixed predefined partition. Arguably, histograms with adaptive partition, for example V-optimal and equi

, in cases where subdimensional datasets are accessed, this leads to significant transmission inefficiency. As an alternative, we propose novel server-client based data representation and retrieval methods which can be used for fast random access of low dimensional data from high dimensional datasets

Abstract Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami op erator on a manifold , and the connections to the heat equation , we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifold embedded

One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data. We consider the problem of constructing a representation for data lying on a low-dimensional manifold embedded in a high-dimensional space. Drawing

, small balanced separators can be found efficiently. Faithful low-dimensional representations of statistical data allow for meaningful and efficient clustering, which is one of the most basic tasks in pattern-recognition. For the (mostly heuristic) methods used

by focusing on low-dimensional structures that approximate or encapsulate given high-dimensional data. Cutting edge methods of dimension reduction were brought together from probability and statistics, geom-etry, topology, and computer science. These techniques included variable selection, graphical modeling

In this paper we describe a new tool for interactive free-form fair surface design. By generalizing classical discrete Fourier analysis to two-dimensional discrete surface signals -- functions defined on polyhedral surfaces of arbitrary topology --, we reduce the problem of surface smoothing

a two-dimensional Cartesian histogram gridas a world model. This world model is updated continuously with range data sampled by on-board range sensors. The VFH method subsequently employs a two-stage data-reduction process in order to compute the desired control commands for the vehicle