A dynamic field approach to goal inference and error monitoring for human-robot interaction

• Autor(a) principal: Bicho, E.
• Data de Publicação: 2009
• Outros Autores: Louro, Luís, Hipólito, Nzoji, Erlhagen, Wolfram
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/1822/10306
• topic: Human-robot interaction; Goal inference; Error monitoring; Joint action; Anticipatory behavior; Action understanding; Dynamic neural fields

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.