Adaptive Behavior, 6 (3/4)

Psychological experiments on children's development of spatial knowledge suggest that experience at self-locomotion and visual tracking are important factors. Yet, the mechanism underlying development is unknown. We propose a robot that learns to track a target object mentally (i.e., maintaining a representation of an object's position when outside the field of view) as a model for spatial development. Mental tracking is considered as prediction of an object's position, given the previous environmental state and motor commands and the current environment state resulting from movement. Following Jordan and Rumelhart's (1992) forward modeling architecture, the system consists of two components: an inverse model of sensory input to desired commands and a forward model of motor commands to desired sensory input (goals). The robot was tested on the "three cups" paradigm (in which children are required, under various movement conditions, to select the cup containing the hidden object). Consistent with child development, in the absence of the capacity for self-locomotion, the robot makes errors that are self-center-based. When given the ability for self-locomotion, the robot responds allocentrically.

Key Words

cognitive development; robot learning; egocentric; allocentric

Pages 393-410

Robust Spatial Navigation in a Robot Inspired by Chemotaxis in Caenorhabditis elegans

By Thomas M. Morse, Thomas C. Ferrée, and Shawn R. Lockery

Abstract

We report on the design and implementation of an autonomous robot that performs phototaxis under the control of a simulated neural network. The mechanical configuration of the robot and its neural network controller are patterned after those believed to produce chemotaxis in the nematode Caenorhabditis elegans. The network is first optimized to produce phototaxis in a simulated, nematode-like robot and then is tested on a real robot. We find that both the simulated and real robot perform reliably, making nearly identical trajectories for similar environments and similar starting conditions. Furthermore, their performance is robust to significant perturbations of the robot's locomotion parameters. Finally, we discuss the implicit computational rule that this network uses to control phototaxis. This makes the results intuitive and improves our intuition about control of tactic behavior in two dimensions.

Key Words

robot; nematode; chemotaxis; phototaxis; neural network; robustness

Pages 411-434

Computational Studies of Exploration by Smell

By Wee Kheng Leow

Abstract

Research on exploratory and searching behavior of animals and robots has attracted an increasing amount of interest recently. Existing works have focused mostly on exploratory behavior guided by vision and audition. Research on smell-guided exploration has been lacking, even though animals may use the sense of smell more widely than sight or hearing to search for food and to evade danger.

This article contributes to the study of smell-guided exploration. It describes a series of increasingly complex neural networks, each of which allows a simulated creature to search for food and to evade danger by using smell. Other behaviors such as obstacle negotiation and risk taking emerge naturally from the creature's interaction with the environment. Comparative studies of these networks show that there is no significant performance advantage for a creature to have more than two sensors. This result may help to explain why real animals have only one or two smell-sensing organs.

Key Words

olfactory-motor coordination; exploration by smell; obstacle negotiation; danger avoidance; risk taking; emergent behaviors

Pages 435-471

Affordances, Motivations, and the World Graph Theory

By Alex Guazzelli, Fernando J. Corbacho, Mihail Bota, and Michael A. Arbib

Abstract

O'Keefe and Nadel (1978) distinguish two paradigms for navigation, the "locale system" for map-based navigation and the "taxon (behavioral orientation) system" for route navigation. This article models the taxon system, the map-based system, and their interaction, and argues that the map-based system involves the interaction of hippocampus and other systems.

We relate taxes to the notion of an affordance. Just as a rat may have basic taxes for approaching food or avoiding a bright light, so does it have a wider repertoire of affordances for possible actions associated with immediate sensing of its environment. We propose that affordances are extracted by the rat posterior parietal cortex, which guides action selection by the premotor cortex and is influenced also by hypothalamic drive information.

The taxon-affordances model (TAM) for taxon-based determination of movement direction is based on models of frog detour behavior, with expectations of future reward implemented using reinforcement learning. The specification of the direction of movement is refined by current affordances and motivational information to yield an appropriate course of action.

The world graph (WG) theory expands the idea of a map by developing the hypothesis that cognitive and motivational states interact. This article describes an implementation of this theory, the WG model. The integrated TAM-WG model then allows us to explain data on the behavior of rats with and without fornix lesions, which disconnect the hippocampus from other neural systems.

Key Words

affordance; navigation; motivation; hippocampus; parietal cortex; reinforcement learning

Pages 473-507

Ecological Robotics

By Andrew P. Duchon, William H. Warren, Leslie Pack Kaelbling

Abstract

There are striking parallels between ecological psychology and the new trends in robotics and computer vision, particularly regarding how agents interact with the environment. We present some ideas from ecological psychology, including control laws using optic flow, affordances, and action modes, and describe our implementation of these concepts in two mobile robots that can avoid obstacles and chase or flee moving targets solely by using optic flow. The properties of these methods were explored further in simulation. This work ties in with that of others who argue for a methodological approach in robotics that forgoes a central model or planner. Not only might ecological psychology contribute to robotics, but robotic implementations might, in turn, provide a test bed for ecological principles and a source of ideas that could be tested in animals and humans.

Key Words

ecological psychology; behavior-based robotics; optic flow; obstacle avoidance; tag

Pages 509-534

Cognitive Models of Spatial Navigation from a Robot Builder's Perspective

By Gordon Wyeth and Brett Browning

Abstract

Complete physically embodied agents present a powerful medium for the investigation of cognitive models for spatial navigation. This article presents a maze-solving robot, called a micromouse, that parallels many of the behaviors found in its biological counterpart, the rat. A cognitive model of the robot is presented, and its limits are investigated. Limits are found to exist with respect to biological plausibility and robot applicability. It is proposed that the fundamental representations used to store and process information are the limiting factor. A review of the literature of current cognitive models reveals a lack of models suitable for implementation in real agents and proposes that available models fail as they have not been developed with real agents in mind. A solution to this conundrum is proposed in a list of guidelines for the development of future spatial models.

Key Words

cognitive model; spatial navigation; representation; schema; motivation

Pages 535-566

Self-Orienting with On-Line Learning of Environmental Features

By Guang Li, Bertil Svensson, and Anders Lansner

Abstract

Evidence from recently conducted neurophysiological experiments on freely moving rats has revealed that the firing of the head-direction cell ensemble predicts the future head direction in response to the vestibular input and that visual cues strongly influence the shift of the tuning curve represented by the firing of the head-direction cell ensemble. In this article, we investigate the possibility of using learned landmark features to self-orient an autonomous agent in a partially known environment. A model is suggested that incorporates an artificial head-direction system for emulating the behavior of head-direction cell ensembles in biological systems, a lattice-based dynamic cell structure for categorizing and classifying environmental features, and an expectancy-based learning mechanism that learns to associate each head direction with a certain environmental feature. Our experimental results show that the suggested model is capable of correcting the drift in the orientation estimated by dead-reckoning.

Key Words

head-direction system emulation; self-orientation; dynamic cell structure; on-line learning; head-direction drift calibration; autonomous mobile robot

Pages 567-569

Author Index to Volume 6

Pages 571-575

Key Word Index to Volume 6

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Adaptive Behavior, 6 (3/4)

Adaptive Behavior

Volume 6, Number 3-4

Winter/Spring 1998

Table of Contents

Nestor A. Schmajuk

Introduction to the Special Issue: Biologically Inspired Models of Navigation

Kazuo Hiraki, Akio Sashima, and Steven Phillips

From Egocentric to Allocentric Spatial Behavior: A Computational Model of Spatial Development