2,680 research outputs found
Evolutionary Robots with on-line self-organization and behavioral fitness
We address two issues in Evolutionary Robotics, namely the genetic encoding and the performance criterion, also known as fitness function. For the first aspect, we suggest to encode mechanisms for parameter self-organization, instead of the parameters themselves as in conventional approaches. We argue that the suggested encoding generates systems that can solve more complex tasks and are more robust to unpredictable sources of change. We support our arguments with a set of experiments on evolutionary neural controller for physical robots and compare them to conventional encoding. In addition, we show that when also the genetic encoding is left free to evolve, artificial evolution will select to exploit mechanisms of self-organization. For the second aspect, we shall discuss the role of the performance criterion, also known as fitness function, and suggest Fitness Space as a framework to conceive fitness functions in Evolutionary Robotics. Fitness Space can be used as a guide to design fitness functions as well as to compare different experiments in Evolutionary Robotics
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
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.Comment: 23 pages, 1 figure, 1 tabl
Scalable Co-Optimization of Morphology and Control in Embodied Machines
Evolution sculpts both the body plans and nervous systems of agents together
over time. In contrast, in AI and robotics, a robot's body plan is usually
designed by hand, and control policies are then optimized for that fixed
design. The task of simultaneously co-optimizing the morphology and controller
of an embodied robot has remained a challenge. In psychology, the theory of
embodied cognition posits that behavior arises from a close coupling between
body plan and sensorimotor control, which suggests why co-optimizing these two
subsystems is so difficult: most evolutionary changes to morphology tend to
adversely impact sensorimotor control, leading to an overall decrease in
behavioral performance. Here, we further examine this hypothesis and
demonstrate a technique for "morphological innovation protection", which
temporarily reduces selection pressure on recently morphologically-changed
individuals, thus enabling evolution some time to "readapt" to the new
morphology with subsequent control policy mutations. We show the potential for
this method to avoid local optima and converge to similar highly fit
morphologies across widely varying initial conditions, while sustaining fitness
improvements further into optimization. While this technique is admittedly only
the first of many steps that must be taken to achieve scalable optimization of
embodied machines, we hope that theoretical insight into the cause of
evolutionary stagnation in current methods will help to enable the automation
of robot design and behavioral training -- while simultaneously providing a
testbed to investigate the theory of embodied cognition
Evolution of collective behaviors for a real swarm of aquatic surface robots
Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.info:eu-repo/semantics/publishedVersio
Engineering evolutionary control for real-world robotic systems
Evolutionary Robotics (ER) is the field of study concerned with the application
of evolutionary computation to the design of robotic systems. Two main
issues have prevented ER from being applied to real-world tasks, namely scaling to
complex tasks and the transfer of control to real-robot systems. Finding solutions
to complex tasks is challenging for evolutionary approaches due to the bootstrap
problem and deception. When the task goal is too difficult, the evolutionary process
will drift in regions of the search space with equally low levels of performance
and therefore fail to bootstrap. Furthermore, the search space tends to get rugged
(deceptive) as task complexity increases, which can lead to premature convergence.
Another prominent issue in ER is the reality gap. Behavioral control is typically
evolved in simulation and then only transferred to the real robotic hardware when
a good solution has been found. Since simulation is an abstraction of the real
world, the accuracy of the robot model and its interactions with the environment
is limited. As a result, control evolved in a simulator tends to display a lower
performance in reality than in simulation.
In this thesis, we present a hierarchical control synthesis approach that enables
the use of ER techniques for complex tasks in real robotic hardware by mitigating
the bootstrap problem, deception, and the reality gap. We recursively decompose
a task into sub-tasks, and synthesize control for each sub-task. The individual
behaviors are then composed hierarchically. The possibility of incrementally
transferring control as the controller is composed allows transferability issues to
be addressed locally in the controller hierarchy. Our approach features hybridity,
allowing different control synthesis techniques to be combined. We demonstrate
our approach in a series of tasks that go beyond the complexity of tasks where ER
has been successfully applied. We further show that hierarchical control can be applied
in single-robot systems and in multirobot systems. Given our long-term goal
of enabling the application of ER techniques to real-world tasks, we systematically
validate our approach in real robotic hardware. For one of the demonstrations in
this thesis, we have designed and built a swarm robotic platform, and we show the
first successful transfer of evolved and hierarchical control to a swarm of robots
outside of controlled laboratory conditions.A Robótica Evolutiva (RE) é a área de investigação que estuda a aplicação de
computação evolutiva na conceção de sistemas robóticos. Dois principais desafios
têm impedido a aplicação da RE em tarefas do mundo real: a dificuldade em solucionar
tarefas complexas e a transferência de controladores evoluídos para sistemas
robóticos reais. Encontrar soluções para tarefas complexas é desafiante para as
técnicas evolutivas devido ao bootstrap problem e à deception. Quando o objetivo
é demasiado difícil, o processo evolutivo tende a permanecer em regiões do espaço
de procura com níveis de desempenho igualmente baixos, e consequentemente não
consegue inicializar. Por outro lado, o espaço de procura tende a enrugar à medida
que a complexidade da tarefa aumenta, o que pode resultar numa convergência
prematura. Outro desafio na RE é a reality gap. O controlo robótico é tipicamente
evoluído em simulação, e só é transferido para o sistema robótico real quando uma
boa solução tiver sido encontrada. Como a simulação é uma abstração da realidade,
a precisão do modelo do robô e das suas interações com o ambiente é limitada,
podendo resultar em controladores com um menor desempenho no mundo real.
Nesta tese, apresentamos uma abordagem de síntese de controlo hierárquica
que permite o uso de técnicas de RE em tarefas complexas com hardware robótico
real, mitigando o bootstrap problem, a deception e a reality gap. Decompomos
recursivamente uma tarefa em sub-tarefas, e sintetizamos controlo para cada subtarefa.
Os comportamentos individuais são então compostos hierarquicamente.
A possibilidade de transferir o controlo incrementalmente à medida que o controlador
é composto permite que problemas de transferibilidade possam ser endereçados
localmente na hierarquia do controlador. A nossa abordagem permite
o uso de diferentes técnicas de síntese de controlo, resultando em controladores
híbridos. Demonstramos a nossa abordagem em várias tarefas que vão para além
da complexidade das tarefas onde a RE foi aplicada. Também mostramos que o
controlo hierárquico pode ser aplicado em sistemas de um robô ou sistemas multirobô.
Dado o nosso objetivo de longo prazo de permitir o uso de técnicas de
RE em tarefas no mundo real, concebemos e desenvolvemos uma plataforma de
robótica de enxame, e mostramos a primeira transferência de controlo evoluído e
hierárquico para um exame de robôs fora de condições controladas de laboratório.This work has been supported by the Portuguese Foundation for Science
and Technology (Fundação para a Ciência e Tecnologia) under the grants
SFRH/BD/76438/2011, EXPL/EEI-AUT/0329/2013, and by Instituto de Telecomunicações
under the grant UID/EEA/50008/2013
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