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.
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- Partial requirement for: M.S., Arizona State University, 2017Note typethesis
- Includes bibliographical references (pages 38-40)Note typebibliography
- Field of study: Computer science