Improving Grasp Skills Using Schema Structured Learning

Abstract

In the control-based approach to robotics, complexbehavior is created by sequencing and combining control primitives.While it is desirable for the robot to autonomously learn thecorrect control sequence, searching through the large number ofpotential solutions can be time consuming. This paper constrainsthis search to variations of a generalized solution encoded ina framework known as an action schema. A new algorithm,schema structured learning, is proposed that repeatedly executesvariations of the generalized solution in search of instantiationsthat satisfy action schema objectives. This approach is testedin a grasping task where Dexter, the UMass humanoid robot,learns which reaching and grasping controllers maximize theprobability of grasp success.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
AD1126846

Entities

People

  • Andrew H. Fagg
  • Robert Platt
  • Roderic A. Grupen

Organizations

  • University of Massachusetts Amherst

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Artificial Intelligence
  • Computer Programs
  • Computer Science
  • Computers
  • Control
  • Data Displays
  • Dynamic Programming
  • Equations
  • Human Behavior
  • Learning
  • Load Cells
  • Orientation (Direction)
  • Probability
  • Robotics
  • Robots
  • Trajectories
  • Transitions
  • Universities

Fields of Study

  • Computer science

Readers

  • Database Systems and Applications
  • Operations Research
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Information Retrieval
  • AI & ML - Machine Learning Algorithms
  • Autonomy