On-line Robot Adaptation to Environmental Change

Abstract

Robots performing tasks constantly encounter changing environmental conditions. These changes in the environment vary from the dramatic, such as rearrangement of furniture, to the subtle, such as a burnt out light bulb or a different carpeting. We do not recognize many of these changes, especially subtle changes, but robots do. These changes often lead to the failure of robots. In this thesis, we develop an algorithm for detecting these changes. Traditional sensor models do not capture all of the dependencies in the sensor data and are not capable of detecting all types of signal changes while maintaining a strong probabilistic foundation. This thesis corrects these shortcomings. We show how detecting the current conditions in which the robot is operating can lead to increased performance and lower failure rates. The methods in this thesis are tested on real tasks performed by a real robot, namely a Sony AIBO robot.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2005
Accession Number
ADA457142

Entities

People

  • Scott Lenser

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Aircrafts
  • Bayesian Networks
  • Climate Change
  • Computational Science
  • Detectors
  • Dimensionality Reduction
  • Hidden Markov Models
  • Information Science
  • Machine Learning
  • Markov Models
  • Network Science
  • Neural Networks
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Reliability
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Robotics and Automation.
  • Sensor Fusion and Tracking Systems.
  • Systems Analysis and Design

Technology Areas

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