Fast learning mapping schemes for robotic hand-eye coordination

Martin Siegfried Hülse, Sebastian Daryl McBride, Mark Howard Lee

Research output: Contribution to journalArticlepeer-review

18 Citations (SciVal)
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In aiming for advanced robotic systems that autonomously and permanently readapt to changing and uncertain environments,we introduce a scheme of fast learning and readaptation of robotic sensorimotor mappings based on biological mechanisms underpinning the development and maintenance of accurate human reaching. The study presents a range of experiments, using two distinct computational architectures, on both learning and realignment of robotic hand-eye coordination. Analysis of the results provide insights into the putative parameters and mechanisms required for fast readaptation and generalization from both a robotic and biological perspective.
Original languageEnglish
Pages (from-to)1 - 16
Number of pages16
JournalCognitive Computation
Issue number1
Early online date11 Dec 2009
Publication statusPublished - Mar 2010


  • Hand–eye coordination
  • Mapping
  • Cross-modal
  • Robotics
  • Realignment
  • Learning


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