Article

A biologically inspired meta-control navigation system for the Psikharpax rat robot

Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie, 4 place Jussieu, 75005 Paris, France.
Bioinspiration & Biomimetics (Impact Factor: 2.35). 06/2012; 7(2):025009. DOI: 10.1088/1748-3182/7/2/025009
Source: PubMed

ABSTRACT

A biologically inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e.g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulations. But the capacity of the model to work on a real robot platform had not been tested. This paper presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cue-guided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment-recognized as new contexts-and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performance and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such a brain-inspired meta-controller may provide an advancement for learning architectures in robotics.

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    • "This works well in simple simulated tasks with less than 10 states but does not scale-up to real-world robotic tasks involving hundreds of states. Keramati and colleagues [7] proposed to avoid estimating the uncertainty of the MB system by considering that it always has " perfect information " , which again cannot be true in tasks where the large number of states imposes to compute approximations of the transition function, as we previously found in a robotic navigation implementation of these learning processes [3]. In a recent work, we proposed a new neuro-inspired cognitive architecture combining MB and MF RL applied to a robotic cube-pushing task [11] and a human-robot cooperation task [10]. "
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    • " MB systems are then updated according to the action a taken by the full model in state s – even if the systems would have individ - ually favoured different actions – and the resulting new state s 0 and retrieved reward r , as previously done in other computational mod - els involving a cooperation between model - free and model - based systems ( Caluwaerts et al . , 2012 ) ."
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