Learning Task Sequences from Scratch: Applications to the Control of Tools and Toys by a Humanoid Robot

Abstract

The goal of this work is to build perceptual and motor control systems for a humanoid robot, starting from an infant's early ability for detecting repetitive or abruptly varying world events from human-robot interactions, and walking developmentally towards robust perception and learning. This paper presents strategies for learning task sequences from human-robot interaction cues. Demonstration by human teachers facilitates robot learning to recognize new objects, such as tools or toys, and their functionality. Self-exploration of the world extends the robot's knowledge concerning object properties. Multi-modal percepts are then acquired and recognized by robotic manipulation of toys and tools.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2004
Accession Number
ADA434681

Entities

People

  • Artur M. Arsenio

Organizations

  • Massachusetts Institute of Technology

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Automata Theory
  • Computational Science
  • Computer Science
  • Computer Vision
  • Control Systems
  • Frequency
  • Human-Robot Interaction
  • Intelligent Agents
  • Intelligent Systems
  • Machine Learning
  • Mathematical Analysis
  • Nonlinear Dynamics
  • Object Recognition
  • Recognition
  • Robotics

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computer Vision.

Technology Areas

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