Goal-oriented robot navigation learning using a multi-scale space representation

M. Llofriu, G. Tejera, M. Contreras, T. Pelc, J. M. Fellous, A. Weitzenfeld

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


There has been extensive research in recent years on the multi-scale nature of hippocampal place cells and entorhinal grid cells encoding which led to many speculations on their role in spatial cognition. In this paper we focus on the multi-scale nature of place cells and how they contribute to faster learning during goal-oriented navigation when compared to a spatial cognition system composed of single scale place cells. The task consists of a circular arena with a fixed goal location, in which a robot is trained to find the shortest path to the goal after a number of learning trials. Synaptic connections are modified using a reinforcement learning paradigm adapted to the place cells multi-scale architecture. The model is evaluated in both simulation and physical robots. We find that larger scale and combined multi-scale representations favor goal-oriented navigation task learning.

Original languageEnglish (US)
Pages (from-to)62-74
Number of pages13
JournalNeural Networks
StatePublished - 2015


  • Hippocampus
  • Multiscale spatial representation
  • Place cells
  • Reinforcement learning
  • Spatial cognition model

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence


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