A Robot that Walks; Emergent Behaviors from a Carefully Evolved Network

Abstract

Most animals have significant behavioral expertise built in without having to explicitly learn it all from scratch. This expertise is a product of evolution of the organisms; it can be viewed as a very long term form of learning which provides a structured system within which individuals might learn more specialized skills or abilities. This paper suggests one possible mechanism for analogous robot evolution by describing a carefully designed series of networks, each one being a strict augmentation of the previous one, which control a six legged walking machine capable of walking over rough terrain and following a person passively sensed in the infrared spectrum. As the completely decentralized networks are augmented, the robot's performance and behavior repertoire demonstrably improve. The rationale for such demonstrations is that they may provide a hint as to the requirements for automatically building massive networks to carry out complex sensory-motor tasks. The experiments with an actual robot ensure that an essence of reality is maintained and that no critical disabling problems have been ignored.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1989
Accession Number
ADA207958

Entities

People

  • Rodney A. Brooks

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Sensors

DTIC Thesaurus Topics

  • Abstracts
  • Artificial Intelligence
  • Biomechanical Phenomena
  • Detectors
  • Diagrams
  • Engineering
  • Information Systems
  • Infrared Detectors
  • Massachusetts
  • Military Research
  • Networks
  • Robots
  • Security
  • Sensor Fusion
  • Silver Zinc Batteries
  • Standards
  • Wiring Diagrams

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Robotics and Automation.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Neural Networks
  • Autonomy