CAD-based 3-D object recognition

Journal ArticleWe propose an approach to 3-D object recognition using CAD-based geometry models for freeform surfaces. Geometry is modeled with rational B-splines by defining surface patches and then combining these into a volumetric model of the object. Characteristic features are then extracted from this model and subjected to a battery of tests to select an "optimal" subset of surface features which are robust with respect to the sensor being used (e.g. laser range finder versus passive stereo) and permit recognition of the object from any viewing position. These features are then organized into a "strategy tree" which defines the order in which the features are sought, and any corroboration required to justify issuing a hypotheses. We propose the use of geometric sensor data integration techniques as a means for formally selecting surface features on free-form objects in order to build recognition strategies. Previous work has dealt with polyhedra and generalized cylinders, whereas here we propose to apply the method to more general surfaces

Manipulability-Based Spatial Isotropy: A Kinematic Reflex

This paper reports results obtained from a reflexive robot controller designed to manage the posture of a 20 degree-of-freedom (DOF) hand/arm system performing grasping tasks. The goal is to provide a framework for sensor-based controllers that dynamically optimize the kinematic configuration of the robot to improve performance on the grasping task. We will present such a behavior and illustrate how it is used to suppress local errors in a priori models. Introduction A great deal of work has focused on developing cooperative and dextrous manipulation systems. This domain is composed of independently challenging issues: dimensionality, redundant degrees of freedom, uncertainty resolution in modeling, planning, and execution. Dimensionality The "find-path" problem for configuration spaces obstructed by polynomially described boundary (given perfect information) has already been completely solved by Schwartz and Shahir[14]. A nearly optimal solution has been suggested by Canny for t..

A Framework for learning declarative structure

Abstract — This paper provides a framework with which a humanoid robot can efficiently learn complex behavior. In this framework, a robot is rewarded by learning how to generate novel sensorimotor feedback—a form of native motivation. This intrinsic drive biases the robot to learn increasingly complex knowledge about itself and its effect on the environment. The framework includes a mechanism for uncovering hidden state in a well-structured state and action space. We present an example wherein the robot, Dexter, learns a sequence of manual skills: (1) searching for and grasping an object, (2) the length of its arms, and (3) how to portray its intentions to human teachers in order to induce them to help. I