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

By Bicho, E.; Louro, L.; Hipólito, N.; Erlhagen, W.

Adaptive and Emergent Behaviour and Complex Systems - Proceedings of the 23rd Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB 2009



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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