Robot Imitation Learning of High-Level Planning Information

Abstract

We present a system that enables a robot to learn to plan through demonstration and imitation. An imitator acquires planning operators by observing a demonstrators, segmenting the demonstrators actions into planning steps, and learning the preconditions and effects of the operators. When the imitator tries to execute its own plans, it learns to perform the operations through reinforcement learning, and corrects errors in the previously learned operator effects.

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Document Details

Document Type
Technical Report
Publication Date
May 02, 2005
Accession Number
ADA460420

Entities

People

  • Frderick L. Crabbe
  • Rebecca Hwa

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Computer Science
  • Computers
  • Information Operations
  • Learning
  • Psychological Phenomena And Processes
  • Reinforcement Learning
  • Theoretical Computer Science
  • United States Naval Academy

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Enterprise Information Systems Architecture and Joint Command Capability Interoperability Support.
  • Regression Analysis.

Technology Areas

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