The Contribution of Active Body Movement to Visual Development in Evolutionary Robots

Inspired by the pioneering work by Held and Hein (1963) on the development of kitten visuo-motor systems, we explore the role of active body movement in the developmental process of the visual system by using robots. The receptive fields in an evolved mobile robot are developed during active or passive movement with a Hebbian learning rule. In accordance to experimental observations in kittens, we show that the receptive fields and behavior of the robot developed under active condition significantly differ from those developed under passive condition. A possible explanation of this difference is derived by correlating receptive field formation and behavioral performance in the two conditions.

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Dario Floreano, Mototaka Suzuki
2005
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https://infoscience.epfl.ch/record/63992?ln=fr

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Concepts associés (11)

Concepts associés

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Publications associées (25)

Publications associées

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Constraints on Body Movement during Visual Development Affect Behavior of Evolutionary Robots

Dario Floreano, Mototaka Suzuki

We explore the role of active body movement in the developmental process of the visual system. Receptive fields of an evolved mobile robot are developed during active or passive movement with the generalized Hebbian algorithm (Sanger, 1989). In accordance to experimental observations of kitten, we show that the receptive fields and behavior of the robot developed under active condition significantly differ from those developed under passive condition. A possible explanation of this difference is derived by correlating receptive field formation and behavioral performance in the two conditions.

2005

Chargement

Active Vision and Receptive Field Development in Evolutionary Robots

Dario Floreano, Claudio Mattiussi, Mototaka Suzuki

In this paper, we describe the artificial evolution of adaptive neural controllers for an outdoor mobile robot equipped with a mobile camera. The robot can dynamically select the gazing direction by moving the body and/or the camera. The neural control system, which maps visual information to motor commands, is evolved online by means of a genetic algorithm, but the synaptic connections (receptive fields) from visual photoreceptors to internal neurons can also be modified by Hebbian plasticity while the robot moves in the environment. We show that robots evolved in physics-based simulations with Hebbian visual plasticity display more robust adaptive behavior when transferred to real outdoor environments as compared to robots evolved without visual plasticity. We also show that the formation of visual receptive fields is significantly and consistently affected by active vision as compared to the formation of receptive fieldswith grid sample images in the environment of the robot. Finally, we show that the interplay between active vision and receptive field formation amounts to the selection and exploitation of a small and constant subset of visual features available to the robot.

2005

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Enactive Robot Vision

Dario Floreano, Mototaka Suzuki

Enactivism claims that sensory-motor activity and embodiment are crucial in perceiving the environment and that machine vision could be a much simpler business if considered in this context. However, computational models of enactive vision are very rare and often rely on handcrafted control systems. In this paper, we describe results from experiments where evolutionary robots can choose whether to exploit sensory motor coordination in a set of vision- based tasks. We show that complex visual tasks can be tackled with remarkably simple neural architectures generated by a co-evolutionary process of active vision and feature selection. We describe the application of this methodology in four sets of experiments, namely shape discrimination, car driving, and wheeled/bipedal robot navigation. A further set of experiments where the visual system can develop the receptive fields by means of unsupervised Hebbian learning, demonstrates that the receptive fields are significantly affected by the behavior of the system and differ from those predicted by most computational models of visual cortex. Finally, we show that our robots can also replicate the performance deficiencies observed in experiments of sensory deprivation with kitten.

2008

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