Embodiment and Manipulation Learning Process for a Humanoid Hand.

Abstract

Babies are born with simple manipulation capabilities such as reflexes to perceived stimuli. Initial discoveries by babies are accidental until they become coordinated and curious enough to actively investigate their surroundings. This thesis explores the development of such primitive learning systems using an embodied light-weight hand with three fingers and a thumb. It is self-contained having four motors and 36 exteroceptor and proprioceptor sensors controlled by an on-palm microcontroller. Primitive manipulation is learned from sensory inputs using competitive learning, back-propagation algorithm and reinforcement learning strategies. This hand will be used for a humanoid being developed at the MIT Artificial Intelligence Laboratory.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1995
Accession Number
ADA299421

Entities

People

  • Yoky Matsuoka

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Circuits
  • Cognition
  • Cognitive Science
  • Computer Languages
  • Computer Science
  • Computers
  • Control Systems
  • Digital Information
  • Electrical Engineering
  • Nervous System
  • Network Science
  • Networks
  • Neural Networks
  • Psychology
  • Reinforcement Learning
  • Self Organizing Systems

Readers

  • Educational Psychology
  • Neural Network Machine Learning.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks