title: From motor babbling to hierarchical learning by imitation: a robot developmental pathway creator: Demiris, Yiannis creator: Dearden, Anthony subject: Machine Learning subject: Robotics description: How does an individual use the knowledge acquired through self exploration as a manipulable model through which to understand others and benefit from their knowledge? How can developmental and social learning be combined for their mutual benefit? In this paper we review a hierarchical architecture (HAMMER) which allows a principled way for combining knowledge through exploration and knowledge from others, through the creation and use of multiple inverse and forward models. We describe how Bayesian Belief Networks can be used to learn the association between a robot’s motor commands and sensory consequences (forward models), and how the inverse association can be used for imitation. Inverse models created through self exploration, as well as those from observing others can coexist and compete in a principled unified framework, that utilises the simulation theory of mind approach to mentally rehearse and understand the actions of others. publisher: Lund University Cognitive Studies contributor: Berthouze, Luc contributor: Kaplan, Frédéric contributor: Kozima, Hideki contributor: Yano, Hiroyuki contributor: Konczak, Jürgen contributor: Metta, Giorgio contributor: Nadel, Jacqueline contributor: Sandini, Giulio contributor: Stojanov, Georgi contributor: Balkenius, Christian date: 2005 type: Conference Paper type: PeerReviewed format: application/pdf identifier: http://cogprints.org/4961/1/demiris.pdf identifier: Demiris, Yiannis and Dearden, Anthony (2005) From motor babbling to hierarchical learning by imitation: a robot developmental pathway. [Conference Paper] relation: http://cogprints.org/4961/