Utilize este identificador para referenciar este registo: http://hdl.handle.net/1822/39108

TítuloOff-line simulation inspires insight: a neurodynamics approach to efficient robot task learning
Autor(es)Sousa, Emanuel Augusto Freitas
Erlhagen, Wolfram
Ferreira, Flora José Rocha
Bicho, Estela
Palavras-chaveOff-line learning
Adaptive robot
Sequential task
Dynamic neural field model
Persistent neural activity
Social learning
DataFev-2015
EditoraElsevier
RevistaNeural Networks
Resumo(s)There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.
Tipoarticle
URIhttp://hdl.handle.net/1822/39108
DOI10.1016/j.neunet.2015.09.002
ISSN0893-6080
Versão da editorahttp://www.sciencedirect.com/science/article/pii/S089360801500177X
Arbitragem científicayes
AcessoopenAccess
Aparece nas coleções:CMAT - Artigos com arbitragem/Papers with refereeing

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