61 research outputs found
Incremental vision-based topological SLAM
Published versio
Real-time visual loop-closure detection
Published versio
IOP PUBLISHING
Artificial evolution of the morphology and kinematics in a flapping-wing mini-UA
Real-World Evolution Adapts Robot Morphology and Control to Hardware Limitations
For robots to handle the numerous factors that can affect them in the real
world, they must adapt to changes and unexpected events. Evolutionary robotics
tries to solve some of these issues by automatically optimizing a robot for a
specific environment. Most of the research in this field, however, uses
simplified representations of the robotic system in software simulations. The
large gap between performance in simulation and the real world makes it
challenging to transfer the resulting robots to the real world. In this paper,
we apply real world multi-objective evolutionary optimization to optimize both
control and morphology of a four-legged mammal-inspired robot. We change the
supply voltage of the system, reducing the available torque and speed of all
joints, and study how this affects both the fitness, as well as the morphology
and control of the solutions. In addition to demonstrating that this real-world
evolutionary scheme for morphology and control is indeed feasible with
relatively few evaluations, we show that evolution under the different hardware
limitations results in comparable performance for low and moderate speeds, and
that the search achieves this by adapting both the control and the morphology
of the robot.Comment: Accepted to the 2018 Genetic and Evolutionary Computation Conference
(GECCO
Some Experiments on the influence of Problem Hardness in Morphological Development based Learning of Neural Controllers
Natural beings undergo a morphological development process of their bodies
while they are learning and adapting to the environments they face from infancy
to adulthood. In fact, this is the period where the most important learning
pro-cesses, those that will support learning as adults, will take place.
However, in artificial systems, this interaction between morphological
development and learning, and its possible advantages, have seldom been
considered. In this line, this paper seeks to provide some insights into how
morphological development can be harnessed in order to facilitate learning in
em-bodied systems facing tasks or domains that are hard to learn. In
particular, here we will concentrate on whether morphological development can
really provide any advantage when learning complex tasks and whether its
relevance towards learning in-creases as tasks become harder. To this end, we
present the results of some initial experiments on the application of
morpho-logical development to learning to walk in three cases, that of a
quadruped, a hexapod and that of an octopod. These results seem to confirm that
as task learning difficulty increases the application of morphological
development to learning becomes more advantageous.Comment: 10 pages, 4 figure
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
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