Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
Neural Networks - 2006 Special issue: The brain mechanisms of imitation learning
Random movement strategies in self-exploration for a humanoid robot
Proceedings of the 6th international conference on Human-robot interaction
Coupled inverse-forward models for action execution leading to tool-use in a humanoid robot
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
Human behavior understanding for robotics
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
Is that me?: sensorimotor learning and self-other distinction in robotics
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
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In this paper, we present internal simulations as a methodology for human behaviour recognition and understanding. The internal simulations consist of pairs of inverse forward models representing sensorimotor actions. The main advantage of this method is that it both serves for action selection and prediction as well as recognition. We present several human-robot interaction experiments where the robot can recognize the behaviour of the human reaching for objects.