1,274 research outputs found
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.Comment: 10 pages, 4 figure
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.Comment: 9 pages, 3 figure
Automatic Calibration of Artificial Neural Networks for Zebrafish Collective Behaviours using a Quality Diversity Algorithm
During the last two decades, various models have been proposed for fish
collective motion. These models are mainly developed to decipher the biological
mechanisms of social interaction between animals. They consider very simple
homogeneous unbounded environments and it is not clear that they can simulate
accurately the collective trajectories. Moreover when the models are more
accurate, the question of their scalability to either larger groups or more
elaborate environments remains open. This study deals with learning how to
simulate realistic collective motion of collective of zebrafish, using
real-world tracking data. The objective is to devise an agent-based model that
can be implemented on an artificial robotic fish that can blend into a
collective of real fish. We present a novel approach that uses Quality
Diversity algorithms, a class of algorithms that emphasise exploration over
pure optimisation. In particular, we use CVT-MAP-Elites, a variant of the
state-of-the-art MAP-Elites algorithm for high dimensional search space.
Results show that Quality Diversity algorithms not only outperform classic
evolutionary reinforcement learning methods at the macroscopic level (i.e.
group behaviour), but are also able to generate more realistic biomimetic
behaviours at the microscopic level (i.e. individual behaviour).Comment: 8 pages, 4 figures, 1 tabl
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