Velocity-dependent dynamic manipulability

Measures of dynamic manipulability summarize a manipulator 's capacity to generate accelerations for arbitrary tasks, and such measures are useful tools for the design and control of generalpurpose robots. Existing measures, however, downplay the effects of velocity or else ignore them altogether. In this paper we derive the relationship between joint velocity and end-effector acceleration, and through case studies we demonstrate that velocity has a complex, non-negligible effect on manipulability. We also provide evidence that movement near a singularity is beneficial for certain tasks

INTEGRATED CONTROL FOR INTERPRETING AND MANIPULATING THE ROBOT ENVIRONMENT

165-17

A feedback control structure for on-line learning tasks

This paper addresses adaptive control architectures for systems that respond autonomously to changing tasks. Such systems often have many sensory and motor alternatives and behavior drawn from these produces varying quality solutions. The objective is then to ground behavior in control laws which, combined with resources, enumerate closed-loop behavioral alternatives. Use of such controllers leads to analyzable and predictable composite systems, permitting the construction of abstract behavioral models. Here, discrete event system and reinforcement learning techniques are employed to constrain the behavioral alternatives and to synthesize behavior on-line. To illustrate this, a quadruped robot learning a turning gait subject to safety and kinematic constraints is presented. Keywords: Control Composition, DEDS, Reinforcement Learning, Walking. 1 Introduction Behavior generation in complex sensorimotor systems can be viewed as a scheduling problem in which a policy for engaging resour..

A control basis for teaming multifingered grasps

545-55

Feature learning for recognition with Bayesian networks

Many realistic visual recognition tasks are "open" in the sense that the number and nature of the categories to be learned are not initially known, and there is no closed set of training images available to the system. We argue that open recognition tasks require incremental learning methods, and feature sets that are capable of expressing distinctions at any level of specificity or generality. We describe progress toward such a system that is based on an infinite combinatorial feature space. Feature primitives can be composed into increasingly complex and specific compound features. Distinctive features are learned incrementally, and are incorporated into dynamically updated Bayesian network classifiers. Experimental results illustrate the applicability and potential of our approach. 1. Introduction Proc. Fifteenth International Conference on Pattern Recognition (ICPR 2000), 3-8 September 2000, Barcelona, Spain. Copyright c IEEE. During the past decade, considerable progress has..

2-D CONTACT DETECTION AND LOCALIZATION USING PROPRIOCEPTIVE INFORMATION

23-3

THE APPLICATIONS OF HARMONIC-FUNCTIONS TO ROBOTICS

931-94

Distinctive features should be learned

Abstract. Most existing machine vision systems perform recognition based on a xed set of hand-crafted features, geometric models, or eigen-subspace decomposition. Drawing from psychology, neuroscience and in-tuition, we show that certain aspects of human performance in visual discrimination cannot be explained by any of these techniques. We ar-gue that many practical recognition tasks for articial vision systems operating under uncontrolled conditions critically depend on incremen-tal learning. Loosely motivated by visuocortical processing, we present feature representations and learning methods that perform biologically plausible functions. The paper concludes with experimental results gen