2,424 research outputs found
Evolving neural controllers for terrestrial and extraterrestrial locomotion in an artificial quadruped
This study explores the use of a multi-objective evolutionary algorithm for the automatic synthesis of neural controllers for the quadrupedal locomotion of an artificial creature in a 3-dimensional, physics-based environment. The Paretofrontier Differential Evolution (PDE) algorithm is used to generate a Pareto optimal set of artificial neural networks that optimize the conflicting objectives of maximizing locomotion behavior and minimizing neural network complexity. The focus of this artificial life experiment is to firstly evolve embodied locomotion controllers for a physically simulated quadrupedal creature under terrestrial conditions (i.e. simulating Earth's gravity) and then to investigate the performance of the best evolved controller in this physically simulated creature under different extraterrestrial environments (i.e. simulating gravity on planets other than Earth). It was found that under all extraterrestrial conditions the artificial creature was still able to perform the required locomotion task while in the worst case, some minimal locomotion behavior was still achieved
Evolved embodied phase coordination enables robust quadruped robot locomotion
Overcoming robotics challenges in the real world requires resilient control
systems capable of handling a multitude of environments and unforeseen events.
Evolutionary optimization using simulations is a promising way to automatically
design such control systems, however, if the disparity between simulation and
the real world becomes too large, the optimization process may result in
dysfunctional real-world behaviors. In this paper, we address this challenge by
considering embodied phase coordination in the evolutionary optimization of a
quadruped robot controller based on central pattern generators. With this
method, leg phases, and indirectly also inter-leg coordination, are influenced
by sensor feedback.By comparing two very similar control systems we gain
insight into how the sensory feedback approach affects the evolved parameters
of the control system, and how the performances differs in simulation, in
transferal to the real world, and to different real-world environments. We show
that evolution enables the design of a control system with embodied phase
coordination which is more complex than previously seen approaches, and that
this system is capable of controlling a real-world multi-jointed quadruped
robot.The approach reduces the performance discrepancy between simulation and
the real world, and displays robustness towards new environments.Comment: 9 page
Evolvability signatures of generative encodings: beyond standard performance benchmarks
Evolutionary robotics is a promising approach to autonomously synthesize
machines with abilities that resemble those of animals, but the field suffers
from a lack of strong foundations. In particular, evolutionary systems are
currently assessed solely by the fitness score their evolved artifacts can
achieve for a specific task, whereas such fitness-based comparisons provide
limited insights about how the same system would evaluate on different tasks,
and its adaptive capabilities to respond to changes in fitness (e.g., from
damages to the machine, or in new situations). To counter these limitations, we
introduce the concept of "evolvability signatures", which picture the
post-mutation statistical distribution of both behavior diversity (how
different are the robot behaviors after a mutation?) and fitness values (how
different is the fitness after a mutation?). We tested the relevance of this
concept by evolving controllers for hexapod robot locomotion using five
different genotype-to-phenotype mappings (direct encoding, generative encoding
of open-loop and closed-loop central pattern generators, generative encoding of
neural networks, and single-unit pattern generators (SUPG)). We observed a
predictive relationship between the evolvability signature of each encoding and
the number of generations required by hexapods to adapt from incurred damages.
Our study also reveals that, across the five investigated encodings, the SUPG
scheme achieved the best evolvability signature, and was always foremost in
recovering an effective gait following robot damages. Overall, our evolvability
signatures neatly complement existing task-performance benchmarks, and pave the
way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary
figures. Accepted at Information Sciences journal (in press). Supplemental
videos are available online at, see http://goo.gl/uyY1R
Design of artificial neural oscillatory circuits for the control of lamprey- and salamander-like locomotion using evolutionary algorithms
This dissertation investigates the evolutionary design of oscillatory artificial neural
networks for the control of animal-like locomotion. It is inspired by the neural organ¬
isation of locomotor circuitries in vertebrates, and explores in particular the control
of undulatory swimming and walking. The difficulty with designing such controllers
is to find mechanisms which can transform commands concerning the direction and
the speed of motion into the multiple rhythmic signals sent to the multiple actuators
typically involved in animal-like locomotion. In vertebrates, such control mechanisms
are provided by central pattern generators which are neural circuits capable of pro¬
ducing the patterns of oscillations necessary for locomotion without oscillatory input
from higher control centres or from sensory feedback. This thesis explores the space of
possible neural configurations for the control of undulatory locomotion, and addresses
the problem of how biologically plausible neural controllers can be automatically generated.Evolutionary algorithms are used to design connectionist models of central pattern
generators for the motion of simulated lampreys and salamanders. This work is inspired
by Ekeberg's neuronal and mechanical simulation of the lamprey [Ekeberg 93]. The
first part of the thesis consists of developing alternative neural controllers for a similar
mechanical simulation. Using a genetic algorithm and an incremental approach, a
variety of controllers other than the biological configuration are successfully developed
which can control swimming with at least the same efficiency. The same method
is then used to generate synaptic weights for a controller which has the observed
biological connectivity in order to illustrate how the genetic algorithm could be used
for developing neurobiological models. Biologically plausible controllers are evolved
which better fit physiological observations than Ekeberg's hand-crafted model. Finally,
in collaboration with Jerome Kodjabachian, swimming controllers are designed using a
developmental encoding scheme, in which developmental programs are evolved which
determine how neurons divide and get connected to each other on a two-dimensional
substrate.The second part of this dissertation examines the control of salamander-like swimming
and trotting. Salamanders swim like lampreys but, on the ground, they switch to a
trotting gait in which the trunk performs a standing wave with the nodes at the girdles.
Little is known about the locomotion circuitry of the salamander, but neurobiologists
have hypothesised that it is based on a lamprey-like organisation. A mechanical sim¬
ulation of a salamander-like animat is developed, and neural controllers capable of
exhibiting the two types of gaits are evolved. The controllers are made of two neural
oscillators projecting to the limb motoneurons and to lamprey-like trunk circuitry. By
modulating the tonic input applied to the networks, the type of gait, the speed and
the direction of motion can be varied.By developing neural controllers for lamprey- and salamander-like locomotion, this
thesis provides insights into the biological control of undulatory swimming and walking, and shows how evolutionary algorithms can be used for developing neurobiological
models and for generating neural controllers for locomotion. Such a method could potentially be used for designing controllers for swimming or walking robots, for instance
Evolving a Behavioral Repertoire for a Walking Robot
Numerous algorithms have been proposed to allow legged robots to learn to
walk. However, the vast majority of these algorithms is devised to learn to
walk in a straight line, which is not sufficient to accomplish any real-world
mission. Here we introduce the Transferability-based Behavioral Repertoire
Evolution algorithm (TBR-Evolution), a novel evolutionary algorithm that
simultaneously discovers several hundreds of simple walking controllers, one
for each possible direction. By taking advantage of solutions that are usually
discarded by evolutionary processes, TBR-Evolution is substantially faster than
independently evolving each controller. Our technique relies on two methods:
(1) novelty search with local competition, which searches for both
high-performing and diverse solutions, and (2) the transferability approach,
which com-bines simulations and real tests to evolve controllers for a physical
robot. We evaluate this new technique on a hexapod robot. Results show that
with only a few dozen short experiments performed on the robot, the algorithm
learns a repertoire of con-trollers that allows the robot to reach every point
in its reachable space. Overall, TBR-Evolution opens a new kind of learning
algorithm that simultaneously optimizes all the achievable behaviors of a
robot.Comment: 33 pages; Evolutionary Computation Journal 201
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