Analyzing Interactions between Cue-Guided and Place-Based Navigation with a Computational Model of Action Selection: Influence of Sensory Cues and Training

Dollé, Laurent; Sheynikhovich, Denis; Girard, Benoît; Ujfalussy, Balázs; Chavarriaga, Ricardo; Guillot, Agnès

doi:10.1007/978-3-642-15193-4_32

Cited by 3 publications

(4 citation statements)

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“…Another interesting issue relates to a common neuroscience point of view, derived from the initial experimental observations in rats (Packard and McGaugh, 1996), that the observed strategy shifts in a new task are stereotypical, with goal-directed behaviors being expressed first until habitual systems are sufficiently trained to relay the former. When the incremental nature of the goal-directed system is taken into account in the neurorobotic models considered in this section Dollé et al, 2010b), the sequence of behavioral strategy use can be different. For example, when starting from scratch, the agent may first rely on basic behavioral strategies, like exploration and approach of perceived reward sources, while building its internal state and transition model, and then use the goal-directed system when enough knowledge has been gathered so that it becomes useful ).…”

Section: Coordination Of Different Learning Systems Within Robot Cognmentioning

confidence: 99%

Integration of Action, Joint Action and Learning in Robot Cognitive Architectures

Khamassi¹,

Girard²,

Clodic³

et al. 2016

intel

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Intégration de l’action, de l’action conjointe et de l’apprentissage dans les architectures cognitives robotiques. À l’opposé des recherches en Intelligence Artificielle, où des algorithmes conçus pour des problèmes spécifiques peuvent être testés dans des conditions de simulation parfaitement contrôlée, la Robotique a toujours eu à faire face à la nécessité d’intégrer la perception, la décision et l’exécution de l’action pour pouvoir fonctionner sur des plateformes physiques en interaction dans et avec le monde réel. Cette particularité a obligé les roboticiennes et roboticiens à se poser très tôt la question de comment coordonner efficacement différents systèmes de mémoires, différents niveaux de prise de décision et différents processus d’apprentissage dans des architectures cognitives. En conséquence, les propositions d’architectures robotiques ont souvent été confrontées et nourries par les questionnements sur les architectures cognitives tels qu’abordés en Philosophie, en Psychologie, en Neurosciences, et plus généralement en Sciences Cognitives. Dans cet article, nous passons en revue les travaux robotiques qui ont abordé le problème de l’intégration de différents niveaux d’action et de leurs mécanismes d’apprentissage associés (de la planification des actions orientées vers un but aux simples réflexes ; de la surveillance de l’action aux comportements réactifs guidés par la vision). Nous montrons qu’un tel type d’intégration est nécessaire et suffisant pour permettre à un agent artificiel de montrer un niveau basique de surveillance de sa propre performance, et en conséquence de montrer de plus grandes capacités de flexibilité comportementale, d’autonomie et de généralisation à différents environnements. Nous illustrons cette problématique à travers des exemples expérimentaux sur la coordination de systèmes multiples d’apprentissage au sein d’un même robot, et sur l’application à l’action conjointe lors de la coopération homme-robot. Nous trouvons en effet que des mécanismes partiellement similaires d’intégration des niveaux d’action peuvent fonctionner à la fois pour l’action individuelle et pour l’action conjointe. Enfin, nous mettons en lumière certains des succès et des échecs de ces approches robotiques en espérant nourrir et contribuer aux débats similaires sur l’action qui se posent dans les autres champs des Sciences Cognitives.

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Section: Coordination Of Different Learning Systems Within Robot Cognmentioning

confidence: 99%

Integration of Action, Joint Action and Learning in Robot Cognitive Architectures

Khamassi¹,

Girard²,

Clodic³

et al. 2016

intel

Self Cite

View full text Add to dashboard Cite

show abstract

“…In our case however, the input can be noisy with typically large, but meaningless values for a few neurons in the input. When the sparseness function from [41] is applied to such an input, the noise is reinforced, while useful neurons will be ignored.…”

Section: Visual Place Cellsmentioning

confidence: 99%

“…In a first version of the simulation model [33], ad hoc place cells were used, and thus the dimensionality reduction/clustering problem was not addressed. In [41], a model of the hippocampus [38] was used to autonomously create the place cells. It is based on a competitive Hebbianlike learning rule: a number of random place cells are created; during the learning phase, the place cells specialize for particular input patterns using a sparseness-based Hebbian rule, which only allows for the most active input neurons to reinforce their connections.…”

Section: Visual Place Cellsmentioning

confidence: 99%

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

et al. 2012

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Abstract. 200 words 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 simulation. But the capacity of the model to work on a real robot platform had not been tested.This article presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cueguided 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 performances and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such brain-inspired meta-controller may provide an advancement for learning architectures in robotics.A biologically inspired meta-control navigation system for the Psikharpax rat robot 2

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Interactions of spatial strategies producing generalization gradient and blocking: A computational approach

et al. 2018

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We present a computational model of spatial navigation comprising different learning mechanisms in mammals, i.e., associative, cognitive mapping and parallel systems. This model is able to reproduce a large number of experimental results in different variants of the Morris water maze task, including standard associative phenomena (spatial generalization gradient and blocking), as well as navigation based on cognitive mapping. Furthermore, we show that competitive and cooperative patterns between different navigation strategies in the model allow to explain previous apparently contradictory results supporting either associative or cognitive mechanisms for spatial learning. The key computational mechanism to reconcile experimental results showing different influences of distal and proximal cues on the behavior, different learning times, and different abilities of individuals to alternatively perform spatial and response strategies, relies in the dynamic coordination of navigation strategies, whose performance is evaluated online with a common currency through a modular approach. We provide a set of concrete experimental predictions to further test the computational model. Overall, this computational work sheds new light on inter-individual differences in navigation learning, and provides a formal and mechanistic approach to test various theories of spatial cognition in mammals.

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Analyzing Interactions between Cue-Guided and Place-Based Navigation with a Computational Model of Action Selection: Influence of Sensory Cues and Training

Cited by 3 publications

References 20 publications

Integration of Action, Joint Action and Learning in Robot Cognitive Architectures

Integration of Action, Joint Action and Learning in Robot Cognitive Architectures

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

Interactions of spatial strategies producing generalization gradient and blocking: A computational approach

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