Browsing by Author "Heise, Rosanna"

Now showing 1 - 8 of 8
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    DEMONSTRATION INSTEAD OF PROGRAMMING: FOCUSSING ATTENTION IN ROBOT TASK ACQUISITION
    (1989-09-01) Heise, Rosanna
    This thesis is an important advance in making robots more useable. It develops a task acquisition system which demonstrates the feasibility of constructing new programs just from the user leading a robot. The result is ETAR, for Example-based Task Acquisition in Robots, and has been implemented on an Excalibur robot. Any person, who can do a task with the common direct lead mechanism on industrial manipulators, can designate it to the robot through ETAR. Thus, ETAR is an alternative to robot programming. The acquired procedures are not only repeated sequences, but standard assembly tasks such as widget construction and block stacking--tasks with loops, branches, and variables. ETAR is a prototypical machine learning system which begins from user examples on a real robot, requires minimal background knowledge, learns inductively, and generates the task description with the aid of a focussing mechanism. The focussing mechanism forces ETAR to concentrate on important domain objects, thus eliminating useless steps, determining a symbolic translation for the task, finding loops, introducing branches, and inducing functions to merge examples into one general program. Additionally, this thesis contributes to low level robotics. It provides unpublished kinematics for the Excalibur robot. Furthermore, it offers a unique, intuitive introduction to quaternions and describes how they rotate vectors and interpolate orientations more efficiently than matrices. Quaternions are used to obtain straight line motion for the Excalibur robot. Implementing kinematics and motion interpolation was a preliminary requirement to the learning algorithm.
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    DYNAMIC BIAS IS NECESSARY IN REAL WORLD
    (1991-04-01) Heise, Rosanna; MacDonald, Bruce A.
    This paper discusses the bias present in machine learning systems, emphasizing its effect on learnability and complexity. A good bias must allow more concepts to be learned and/or decrease the complexity associated with learning. The paper develops an exhaustive framework for bias, with two important distinctions: \fIstatic\fR versus \fIdynamic\fR and \fIfocus\fR versus \fImagnify\fR. The well-known candidate elimination algorithm (Mitchell) is used to illustrate the framework. Real world learners need dynamic bias. The paper examines two representative systems. \s+2S\s-2TABB (Utgoff) dynamically magnifies the description space where learning would otherwise be impossible. \s+2E\s-2TAR is a prototype for learning robot assembly tasks from examples--a dynamic focusing mechanism reduces both the real world description space and the task construction complexity. Inductive learning must be viewed as a problem of dynamic search control.
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    KINEMATICS OF AN ELBOW MANIPULATOR WITH FOREARM ROTATION: THE EXCALIBUR
    (1988-10-01) Heise, Rosanna; MacDonald, Bruce A.
    General methods and typical specific solutions for robot arm geometry are well-known. This paper presents a detailed solution for a six joint manipulator which has a rotation at mid-forearm rather than a third wrist axis. Details are given indicating how real joint angles relate to those modeled by the more abstract kinematics. All degeneracies are considered and methods for handling them are given. The paper provides a complete tutorial for kinematic modeling with a specific arm.
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    PROGRAMMING BY EXAMPLE: THE HUMAN FACE OF AI
    (1991-04-01) Witten, Ian H.; MacDonald, Bruce A.; Maulsby, David L.; Heise, Rosanna
    Interactive computer users often find themselves repeatedly performing similar tasks that could be acquired automatically from a teacher. This paper presents principles derived from experience in creating four prototype learners: for technical drawing, text editing, office tasks, and robot assembly. A teaching metaphor (a) enables the user to demonstrate a task by performing it manually, (b) helps to explain the learner's limited capabilities in terms of a persona, and (c) allows users to attribute intentionality. Tasks are represented procedurally, and augmented with constraints. Suitable mechanisms for attention focusing are necessary in order to control inductive search. Hidden features of a task should be made explicit so that the learner need not entertain, and search, all possible missing steps. Key features of the interaction are formalized as "felicity conditions" that help a learner by guaranteeing more explicit, consistent information in demonstrations. Systems that are programmed by human instruction can capitalize on appropriate interactive methods to boost the computational limitations of inductive inference.
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    PROGRAMMING ROBOTS BY EXAMPLE
    (1992-05-01) Heise, Rosanna
    This paper presents a prototypical machine learning system \s+1(E\s-1TAR\s+1)\s-1 that acquires programs for robot tasks. The long term goal of this project is to discover how to make computer technology, in particular robots, more useful to (and controllable by) people in general. Rather than require programming expertise, the idea is to build a system which learns robot programs from users' examples. Thus the \s+1E\s-1TAR learning algorithm begins by sampling the robot path while a user physically leads it through the task. A general procedure, possibly containing loops, branches, and variables, is induced from these examples. The \s+1E\s-1TAR algorithm is novel since an implicit focus mechanism is used to control the entire generalization process. The focus forces \s+1E\s-1TAR to concentrate on the important domain objects, thus eliminating useless steps and translating the sampled sequence into a series of robot primitive motions. Loops and branches are introduced as the focus objects repeat or differ. Finally, robot positional variables are introduced as functions of the common characteristics of the objects in the focus. The programs that \s+1E\s-1TAR generates for three tasks --- block stacking, obtaining an object with a certain characteristic, and sorting --- are shown to provide an intuitive feel for the types of tasks that \s+1E\s-1TAR can learn. The paper concludes with a general discussion of the current issues in programming by example and describes how this new learner is related to previous systems in this area. \s+1E\s-1TAR has been implemented on an Excalibur robot.
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    QUATERNIONS AND MOTION INTERPOLATION
    (1988-11-01) Heise, Rosanna; MacDonald, Bruce A.
    This paper explains straight-line interpolation of solid object motion, such as robot end effector translation and rotation. Smoothly changing orientation is accomplished using quaternions- a way of representing every orientation as four numbers (an angle and an axis of rotation). The first portion of the paper clarifies quaternions to provide an intuitive understanding of their role in rotation. Interpolation is then discussed, concluding with some problems in real manipulator implementations. The interpolation method has been tested on an Excalibur robot.
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    ROBOTS ACQUIRING TASKS FROM EXAMPLES
    (1988-12-25) MacDonald, Bruce A.; Heise, Rosanna
    This paper describes a task acquisition system which is being implemented on a six-joint robot. Functions controlling the robot are constructed directly from examples of the user leading it. The numerical robot feedback is passed through a symbolic processing stage to convert it into primitive motion functions. Thereafter, generalization occurs at two levels - the primitive motion function names and the arguments to these primitive functions. The constructed task function may contain loops, conditionals, and variables. All variables are determined from the objects which are manipulated. General algorithms are described, examples are given, and comparisons to existing operator learning systems are presented.