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The agent-based approach emphasizes the importance of learning through organism-environment interaction. This approach is part of a recent trend in computational models of learning and development toward studying autonomous organisms that are embedded in virtual or real environments. In this paper we introduce the concepts of online and offline sampling and highlight the role of online sampling in agent-based models. After comparing the strengths of each approach for modeling particular developmental phenomena and research questions, we describe a recent agent-based model of infant causal perception. We conclude by discussing some of the present limitations of agent-based models and suggesting how these challenges may be addressed. © 2001 Academic Press Computational models of learning and development are playing an increasingly critical role in child development research (Cassidy, 1990;

Existing approaches for learning to control a robot arm rely on supervised methods where correct behavior is explicitly given. It is difficult to learn to avoid obstacles using such methods, however, because examples of obstacle avoidance behavior are hard to generate. This paper presents an alternative approach that evolves neural network controllers through genetic algorithms. No input /output examples are necessary, since neuroevolution learns from a single performance measurement over the entire task of grasping an object. The approach is tested in a simulation of the OSCAR-6 robot arm which receives both visual and sensory input. Neural networks evolved to effectively avoid obstacles at various locations to reach random target locations. 1 Introduction Many industrial tasks such as assembly, packaging, and processing rely heavily on the manipulation and transportation of small components. Robot arms can automate many of these processes and improve the cost efficiency of the oper...

This book is an outgrowth of discussions that got started in at least three workshops sponsored by the National Science Foundation (NSF):.A workshop on neurocontrol and aerospace applications held in October 1990, under joint sponsorship from McDonnell Douglas and the NSF programs in Dynamic Systems and Control and Neuroengineering.A workshop on intelligent control held in October 1990, under joint sponsorship from NSF and the Electric Power Research Institute, to scope out plans for a major new joint initiative in intelligent control involving a number of NSF programs.A workshop on neural networks in chemical processing, held at NSF in January-February 1991, sponsored by the NSF program in Chemical Reaction Processes The goal of this book is to provide an authoritative source for two kinds of information: (1) fundamental new designs, at the cutting edge of true intelligent control, as well as opportunities for future research to improve on these designs; (2) important real-world applications, including test problems that constitute a challenge to the entire control community. Included in this book are a series of realistic test problems, worked out through lengthy discussions between NASA, NetJroDyne, NSF, McDonnell Douglas, and Honeywell, which are more than just benchmarks for evaluating intelligent control designs. Anyone who contributes to solving these problems may well be playing a crucial role in making possible the future development of hypersonic vehicles and subsequently the

this article we introduce an unsupervisedneural architecture for the control of a mobile robot. The mobile robot to be controlled is organized in a tricycle structure. Movement is performed by selection of angular velocities for the motors attached to the two propulsive wheels, as shown in figure 1. Following an initial learning phase, the controller architecture allows movement between arbitrary points through exteroceptive or visual information. It is important to note that rather than learning explicit trajectories, the controller learns the relationship between angular velocities and the magnitude and direction of the resulting movement. This approach solves the inverse kinematic problem, so that visual information in spatial coordinates can generate the appropriate wheel angular velocities to move the mobile robot to a desired goal. The main characteristic of this architecture that distinguishes it from other neural controllers is that it does not require supervision during the training phase. Supervised neural network models [5, 12] require user knowledge to ensure that the environment during learning is statistically representative of the environment encountered during normal operation. This problem becomes critical when it is necessary to operate in unstructured environments or when the conditions of operation change. Another characteristic is that the system can learn continuously, i.e., it is not necessary to separate the learning phase from the operational phase. This property affords incremental and continuous learning, and adaptation to plant changes such as wear and tear of wheels, and other miscalibrations that may result from normal operation. In the next section we summarize the main characteristics of the Vector Associative Map (VAM) model, on which th...

A neurally based adaptive controller for a 6 degrees of freedom (DOF) robot manipulator with only rotary joints and a hand-held camera is described. The task of the system is to place the manipulator directly above an object that is observed by the camera (i.e., 2D hand-eye coordination). The requirement of adaptivity results in a system which does not make use of any inverse kinematics formulas or other detailed knowledge of the plant; instead, it should be self-supervising and adapt on-line. The proposed neural system will directly translate the preprocessed sensory data to joint displacements. It controls the plant in a feedback loop. The robot arm may make a sequence of moves before the target is reached, when in the meantime the network learns from experience. The network is shown to adapt quickly (in only tens of trials) and form a correct mapping from input to output domain. 1 Introduction Traditionally, when a robot manipulator controller receives sensory information based on ...

A self-learning, adaptive control system for a robot arm using a vision system in a feedback loop is described. The task of the control system is to position the end-effector as accurate as possible directly above a target object, so that it can be grasped. The camera of the vision system is positioned in the end-effector and the visual information is used directly to control the robot. Two strategies are presented to solve the problem of obtaining 3D information from a single camera: a) using the size of the target object and b) using information from a sequence of images from the moving camera. In both cases a neural network is trained to perform the desired mapping. 1 Introduction Conventional sensor-based control systems require explicit knowledge of the kinematics and dynamics of the robot and a careful calibration of the sensor This research has been partly sponsored by the Dutch Foundation for Neural Networks. system. In contrast to this approach, we are interested in a se...

A method is presented to learn 3D grasping of objects with unknown dimensions using a monocular eye-in-hand manipulator. From a sequence of images a motion profile is generated to approach the object of unknown size. It is shown that monocular visual information suffices to control the deceleration of the robot manipulator. A strategy for generating learning samples is presented, and simulation results demonstrate the effectiveness of the method. I. Introduction Sensor based robot control systems can overcome many of the difficulties which are caused by unknown or uncertain models of the environment. Also, conventional sensor based control systems require explicit knowledge of the kinematics and dynamics of the robot arm and a careful calibration of the sensor system. We are interested in self-learning and adaptive systems, where an implicit model of the arm and sensor system is learned from the behaviour of the robot. Neurocomputational techniques have been successfully applied in t...

. We have recently introduced a neural controller for a mobile robot that learns both forward and inverse odometry of a differential-drive robot through an unsupervised learning-bydoing cycle. This article introduces an obstacle avoidance module that is integrated into the neural controller. The obstacle avoidance module makes use of sensory information to determine at each instant a desired angle and distance that causes the robot to navigate around obstacles on the way to a final target. Obstacle avoidance is performed in a reactive manner by representing the objects and target in the robot's environment as Gaussian functions. However, the influence of the Gaussians is modulated dynamically on the basis of the robot's behavior in a way that avoids problems with local minima. The proposed module enables the robot to operate successfully with different obstacle configurations, such as corridors, mazes, doors and even concave obstacles. Key Words. mobile robots; obstacle avoid...

Abstract--By the onset of reaching, young infants are already able to keep track of the position of their han by using visual feedback from the target and proprioceptive feedback from the arm. How is this multimodal coordination achieved? We propose that infants learn to coordinate vision and proprioception by using tactile feedback from the target. In order to evaluate this hypothesis, we employ an evolutionarybased learning algorithm as a proxy for trial-and-error sensorimotor development in young infants. A series of simulation studies illustrate how touch (1) help coordinate vision and proprioception, (2) facilitates a efficient reaching strategy, and (3) promotes intermodal recalibration when the coordination is perturbed. We present two developmental predictions generated by the model, and discuss the relative importance of visual and tactile feedback while learning to reach. Index terms--computational model, sensorimotor development, trial-and-error learning, multimodal coordination I.