Abstract
Biologically inspired models for navigation use mechanisms like path integration or sensori-motor learning. This paper describes the use of a proprioceptive working memory to give path integration the potential to store several goals. Then we coupled the path integration working memory to place cell sensori-motor learning to test the potential autonomy this gives to the robot. This navigation architecture intends to combine the benefits of both strategies in order to overcome their drawbacks. The robot uses a low level motivational system based on a simulated physiology. Experimental evaluation is done with a robot in a real environment performing a multi goal navigation task.
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Hasson, C., Gaussier, P. (2010). Path Integration Working Memory for Multi Tasks Dead Reckoning and Visual Navigation. In: Doncieux, S., Girard, B., Guillot, A., Hallam, J., Meyer, JA., Mouret, JB. (eds) From Animals to Animats 11. SAB 2010. Lecture Notes in Computer Science(), vol 6226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15193-4_36
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DOI: https://doi.org/10.1007/978-3-642-15193-4_36
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