Abstract
Animal and robot social interactions are interesting both for ethological studies and robotics. On the one hand, the robots can be tools and models to analyse animal collective behaviours, on the other hand the robots and their artificial intelligence are directly confronted and compared to the natural animal collective intelligence. The first step is to design robots and their behavioural controllers that are capable of socially interact with animals. Designing such behavioural bio-mimetic controllers remains an important challenge as they have to reproduce the animal behaviours and have to be calibrated on experimental data. Most animal collective behavioural models are designed by modellers based on experimental data. This process is long and costly because it is difficult to identify the relevant behavioural features that are then used as a priori knowledge in model building. Here, we want to model the fish individual and collective behaviours in order to develop robot controllers. We explore the use of optimised black-box models based on artificial neural networks (ANN) to model fish behaviours. While the ANN may not be biomimetic but rather bio-inspired, they can be used to link perception to motor responses. These models are designed to be implementable as robot controllers to form mixed-groups of fish and robots, using few a priori knowledge of the fish behaviours. We present a methodology with multilayer perceptron or echo state networks that are optimised through evolutionary algorithms to model accurately the fish individual and collective behaviours in a bounded rectangular arena. We assess the biomimetism of the generated models and compare them to the fish experimental behaviours.
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This work was funded by EU-ICT project ‘ASSISIbf’, no. 601074.
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Cazenille, L., Bredeche, N., Halloy, J. (2018). Evolutionary Optimisation of Neural Network Models for Fish Collective Behaviours in Mixed Groups of Robots and Zebrafish. In: Vouloutsi , V., et al. Biomimetic and Biohybrid Systems. Living Machines 2018. Lecture Notes in Computer Science(), vol 10928. Springer, Cham. https://doi.org/10.1007/978-3-319-95972-6_10
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