Description

To ensure system integrity, robots need to proactively avoid any unwanted physical perturbation that may cause damage to the underlying hardware. In this thesis work, we investigate a machine learning

To ensure system integrity, robots need to proactively avoid any unwanted physical perturbation that may cause damage to the underlying hardware. In this thesis work, we investigate a machine learning approach that allows robots to anticipate impending physical perturbations from perceptual cues. In contrast to other approaches that require knowledge about sources of perturbation to be encoded before deployment, our method is based on experiential learning. Robots learn to associate visual cues with subsequent physical perturbations and contacts.

Reuse Permissions
  • 7.96 MB application/pdf

    Download count: 0

    Details

    Contributors
    Date Created
    • 2017
    Resource Type
  • Text
  • Collections this item is in
    Note
    • Partial requirement for: M.S., Arizona State University, 2017
      Note type
      thesis
    • Includes bibliographical references (pages 38-40)
      Note type
      bibliography
    • Field of study: Computer science

    Citation and reuse

    Statement of Responsibility

    by Indranil Sur

    Machine-readable links