Learning forward models for robots

  • Dearden A
  • Demiris Y
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Forward models enable a robot to predict the ef- fects of its actions on its own motor system and its environment. This is a vital aspect of intelligent be- haviour, as the robot can use predictions to decide the best set of actions to achieve a goal. The ability to learn forward models enables robots to be more adaptable and autonomous; this paper describes a system whereby they can be learnt and represented as a Bayesian network. The robot’s motor system is controlled and explored using ‘motor babbling’. Feedback about its motor system comes from com- puter vision techniques requiring no prior informa- tion to perform tracking. The learnt forward model can be used by the robot to imitate human move- ment.

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  • SCOPUS: 2-s2.0-84880762141
  • SGR: 84880762141
  • PUI: 369417244
  • ISSN: 10450823


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