MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
Random movement strategies in self-exploration for a humanoid robot
Proceedings of the 6th international conference on Human-robot interaction
Internal simulations for behaviour selection and recognition
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
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We propose a computational model based on inverse-forward model pairs for the simulation and execution of actions. The models are implemented on a humanoid robot and are used to control reaching actions with the arms. In the experimental setup a tool has been attached to the left arm of the robot extending its covered action space. The preliminary investigations carried out aim at studying how the use of tools modifies the body scheme of the robot. The system performs action simulations before the actual executions. For each of the arms, predicted end-effector positions are compared with the desired one and the internal pair presenting the lowest error is selected for action execution. This allows the robot to decide on performing an action either with its hand alone or with the one with the attached tool.