A dynamic field approach to goal inference and error monitoring for human-robot interaction
In this paper we present results of our ongoing research on non-verbal human-robot interaction that is heavily inspired by recent experimental findings about the neuro-cognitive mechanisms supporting joint action in humans. The robot control architecture implements the joint coordination of actions...
| Autor principal: | |
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| Outros Autores: | , , |
| Formato: | conferencePaper |
| Idioma: | eng |
| Publicado em: |
2009
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/1822/10306 |
| País: | Portugal |
| Oai: | oai:repositorium.sdum.uminho.pt:1822/10306 |
| Resumo: | In this paper we present results of our ongoing research on non-verbal human-robot interaction that is heavily inspired by recent experimental findings about the neuro-cognitive mechanisms supporting joint action in humans. The robot control architecture implements the joint coordination of actions and goals as a dynamic process that integrates contextual cues, shared task knowledge and the predicted outcome of the user’s motor behavior. The architecture is formalized by a coupled system of dynamic neural fields representing a distributed network of local but connected neural populations with specific functionalities. We validate the approach in a task in which a robot and a human user jointly construct a toy ’vehicle’. We show that the context-dependent mapping from action observation onto appropriate complementary actions allows the robot to cope with dynamically changing joint action situations. This includes a basic form of error monitoring and compensation. |
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