arXiv subjects

Nicolas Bredeche

Publications and source records attributed to Nicolas Bredeche.

24 records · Page 2Linked to original sources

arXiv2018

Modelling zebrafish collective behaviours with multilayer perceptrons optimised by evolutionary algorithms

Collective movements are pervasive behaviours among social organisms and have led to the development of many models. However, modelling animal trajectories and social interactions in simple bounded environments remains a challenge. Moreover, advances in the understanding of the sensory-motor loop and the information processing by animals are leading to revisions of the traditional assumptions made in decision-making algorithms. In this context, we develop a methodology based on artificial neural networks (ANN) to describe the collective motion of small zebrafish groups in a bounded environment. Although ANN models are commonly used in artificial systems they are still under-explored to model animal collective behaviours. Here, we present a methodology to calibrate Multilayer Perceptrons by learning from real fish experimental data. The ANNs are trained using either supervised learning or various forms of evolutionary reinforcement learning methods (using the CMA-ES and NSGA-III algorithms). We reveal that ANN models trained using evolutionary methods are capable of generating realistic collective motions for groups of 5 zebrafish including the tank wall effects, a feature that is lacking in previous models. Finally, we also discuss the benefits of optimised ANNs as candidates for driving robotic lure with biologically realistic behaviour, a method that is becoming increasingly popular to gather data and validate assumptions on collective behaviours.

q-bio.NC↗

arXiv2018

Evolutionary optimisation of neural network models for fish collective behaviours in mixed groups of robots and zebrafish

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.

q-bio.NC↗

arXiv2018

How to Blend a Robot within a Group of Zebrafish: Achieving Social Acceptance through Real-time Calibration of a Multi-level Behavioural Model

We have previously shown how to socially integrate a fish robot into a group of zebrafish thanks to biomimetic behavioural models. The models have to be calibrated on experimental data to present correct behavioural features. This calibration is essential to enhance the social integration of the robot into the group. When calibrated, the behavioural model of fish behaviour is implemented to drive a robot with closed-loop control of social interactions into a group of zebrafish. This approach can be useful to form mixed-groups, and study animal individual and collective behaviour by using biomimetic autonomous robots capable of responding to the animals in long-standing experiments. Here, we show a methodology for continuous real-time calibration and refinement of multi-level behavioural model. The real-time calibration, by an evolutionary algorithm, is based on simulation of the model to correspond to the observed fish behaviour in real-time. The calibrated model is updated on the robot and tested during the experiments. This method allows to cope with changes of dynamics in fish behaviour. Moreover, each fish presents individual behavioural differences. Thus, each trial is done with naive fish groups that display behavioural variability. This real-time calibration methodology can optimise the robot behaviours during the experiments. Our implementation of this methodology runs on three different computers that perform individual tracking, data-analysis, multi-objective evolutionary algorithms, simulation of the fish robot and adaptation of the robot behavioural models, all in real-time.

cs.NE↗

arXiv2018

Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.

cs.NE↗

arXiv2016

Automated optimisation of multi-level models of collective behaviour in a mixed society of animals and robots

Animal and robotic collective behaviours can exhibit complex dynamics that require multi-level descriptions. Here, we are interested in developing a multi-level modeling framework for the use of robots in studies about animal collective decision-making. In this context, using robots can be useful for validating models in silico, inducing calibrated repetitive stimuli to trigger animal responses or modulating and controlling animal collective behaviour. However, designing appropriate biomimetic robotic behaviour faces a major challenge: how to go from the collective decision dynamics observed with animals to an actual algorithmic implementation in robots. In previous work, this was mainly done by hand, often by taking inspiration from human-designed models. Typically, models of behaviour are either macroscopic, differential equations of the population dynamics, or microscopic,explicit spatio-temporal state of each individual. Only microscopic models can easily be implemented as robot controllers. Here, we address the problem of automating the design of lower level description models that can be implemented in robots and exhibit the same collective dynamics as a given higher level model. We apply evolutionary algorithms to simultaneously optimise the parameters of models accounting for different levels of description. This methodology is applied to an experimentally validated shelter-selection problem solved by gregarious insects and robots. We successfully design and calibrate automatically both a microscopic and a hybrid model exhibiting the same dynamics as a macroscopic one. Our framework can be used for multi-level modeling of collective behaviour in animal or robot populations and bio-hybrid systems.

nlin.AO↗

arXiv2013

Roborobo! a Fast Robot Simulator for Swarm and Collective Robotics

Roborobo! is a multi-platform, highly portable, robot simulator for large-scale collective robotics experiments. Roborobo! is coded in C++, and follows the KISS guideline ("Keep it simple"). Therefore, its external dependency is solely limited to the widely available SDL library for fast 2D Graphics. Roborobo! is based on a Khepera/ePuck model. It is targeted for fast single and multi-robots simulation, and has already been used in more than a dozen published research mainly concerned with evolutionary swarm robotics, including environment-driven self-adaptation and distributed evolutionary optimization, as well as online onboard embodied evolution and embodied morphogenesis.

cs.RO↗