4,599 research outputs found
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
Fusing novelty and surprise for evolving robot morphologies
Traditional evolutionary algorithms tend to converge to a single
good solution, which can limit their chance of discovering more
diverse and creative outcomes. Divergent search, on the other hand,
aims to counter convergence to local optima by avoiding selection
pressure towards the objective. Forms of divergent search such as
novelty or surprise search have proven to be beneficial for both
the efficiency and the variety of the solutions obtained in deceptive
tasks. Importantly for this paper, early results in maze navigation
have shown that combining novelty and surprise search yields an
even more effective search strategy due to their orthogonal nature.
Motivated by the largely unexplored potential of coupling novelty
and surprise as a search strategy, in this paper we investigate how
fusing the two can affect the evolution of soft robot morphologies.
We test the capacity of the combined search strategy against objective,
novelty, and surprise search, by comparing their efficiency and
robustness, and the variety of robots they evolve. Our key results
demonstrate that novelty-surprise search is generally more efficient
and robust across eight different resolutions. Further, surprise
search explores the space of robot morphologies more broadly than
any other algorithm examined.peer-reviewe
Discovering Evolutionary Stepping Stones through Behavior Domination
Behavior domination is proposed as a tool for understanding and harnessing
the power of evolutionary systems to discover and exploit useful stepping
stones. Novelty search has shown promise in overcoming deception by collecting
diverse stepping stones, and several algorithms have been proposed that combine
novelty with a more traditional fitness measure to refocus search and help
novelty search scale to more complex domains. However, combinations of novelty
and fitness do not necessarily preserve the stepping stone discovery that
novelty search affords. In several existing methods, competition between
solutions can lead to an unintended loss of diversity. Behavior domination
defines a class of algorithms that avoid this problem, while inheriting
theoretical guarantees from multiobjective optimization. Several existing
algorithms are shown to be in this class, and a new algorithm is introduced
based on fast non-dominated sorting. Experimental results show that this
algorithm outperforms existing approaches in domains that contain useful
stepping stones, and its advantage is sustained with scale. The conclusion is
that behavior domination can help illuminate the complex dynamics of
behavior-driven search, and can thus lead to the design of more scalable and
robust algorithms.Comment: To Appear in Proceedings of the Genetic and Evolutionary Computation
Conference (GECCO 2017
Worldwide Infrastructure for Neuroevolution: A Modular Library to Turn Any Evolutionary Domain into an Online Interactive Platform
Across many scientific disciplines, there has emerged an open opportunity to utilize the scale and reach of the Internet to collect scientific contributions from scientists and non-scientists alike. This process, called citizen science, has already shown great promise in the fields of biology and astronomy. Within the fields of artificial life (ALife) and evolutionary computation (EC) experiments in collaborative interactive evolution (CIE) have demonstrated the ability to collect thousands of experimental contributions from hundreds of users across the glob. However, such collaborative evolutionary systems can take nearly a year to build with a small team of researchers. This dissertation introduces a new developer framework enabling researchers to easily build fully persistent online collaborative experiments around almost any evolutionary domain, thereby reducing the time to create such systems to weeks for a single researcher. To add collaborative functionality to any potential domain, this framework, called Worldwide Infrastructure for Neuroevolution (WIN), exploits an important unifying principle among all evolutionary algorithms: regardless of the overall methods and parameters of the evolutionary experiment, every individual created has an explicit parent-child relationship, wherein one individual is considered the direct descendant of another. This principle alone is enough to capture and preserve the relationships and results for a wide variety of evolutionary experiments, while allowing multiple human users to meaningfully contribute. The WIN framework is first validated through two experimental domains, image evolution and a new two-dimensional virtual creature domain, Indirectly Encoded SodaRace (IESoR), that is shown to produce a visually diverse variety of ambulatory creatures. Finally, an Android application built with WIN, filters, allows users to interactively evolve custom image effects to apply to personalized photographs, thereby introducing the first CIE application available for any mobile device. Together, these collaborative experiments and new mobile application establish a comprehensive new platform for evolutionary computation that can change how researchers design and conduct citizen science online
Open-ended search for environments and adapted agents using MAP-Elites
Creatures in the real world constantly encounter new and diverse challenges
they have never seen before. They will often need to adapt to some of these
tasks and solve them in order to survive. This almost endless world of novel
challenges is not as common in virtual environments, where artificially
evolving agents often have a limited set of tasks to solve. An exception to
this is the field of open-endedness where the goal is to create unbounded
exploration of interesting artefacts. We want to move one step closer to
creating simulated environments similar to the diverse real world, where agents
can both find solvable tasks, and adapt to them. Through the use of MAP-Elites
we create a structured repertoire, a map, of terrains and virtual creatures
that locomote through them. By using novelty as a dimension in the grid, the
map can continuously develop to encourage exploration of new environments. The
agents must adapt to the environments found, but can also search for
environments within each cell of the grid to find the one that best fits their
set of skills. Our approach combines the structure of MAP-Elites, which can
allow the virtual creatures to use adjacent cells as stepping stones to solve
increasingly difficult environments, with open-ended innovation. This leads to
a search that is unbounded, but still has a clear structure. We find that while
handcrafted bounded dimensions for the map lead to quicker exploration of a
large set of environments, both the bounded and unbounded approach manage to
solve a diverse set of terrains
Novelty search for soft robotic space exploration
The use of soft robots in future space exploration is still a far-fetched idea, but an attractive one. Soft robots are inherently compliant mechanisms that are well suited for locomotion on rough terrain as often faced in extra-planetary environments. Depending on the particular application and requirements, the best shape (or body morphology) and locomotion strategy for such robots will vary substantially. Recent developments in soft robotics and evolutionary optimization showed the possibility to simultaneously evolve the morphology and locomotion strategy in simulated trials. The use of techniques such as generative encoding and neural evolution were key to these findings. In this paper, we improve further on this methodology by introducing the use of a novelty measure during the evolution process. We compare fitness search and novelty search in different gravity levels and we consistently find novelty-based search to perform as good as or better than a fitness-based search, while also delivering a greater variety of designs. We propose a combination of the two techniques using fitness-elitism in novelty search to obtain a further improvement. We then use our methodology to evolve the gait and morphology of soft robots at different gravity levels, finding a taxonomy of possible locomotion strategies that are analyzed in the context of space-exploration
Co-optimising Robot Morphology and Controller in a Simulated Open-Ended Environment
Designing robots by hand can be costly and time consuming, especially if the
robots have to be created with novel materials, or be robust to internal or
external changes. In order to create robots automatically, without the need for
human intervention, it is necessary to optimise both the behaviour and the body
design of the robot. However, when co-optimising the morphology and controller
of a locomoting agent the morphology tends to converge prematurely, reaching a
local optimum. Approaches such as explicit protection of morphological
innovation have been used to reduce this problem, but it might also be possible
to increase exploration of morphologies using a more indirect approach. We
explore how changing the environment, where the agent locomotes, affects the
convergence of morphologies. The agents' morphologies and controllers are
co-optimised, while the environments the agents locomote in are evolved
open-endedly with the Paired Open-Ended Trailblazer (POET). We compare the
diversity, fitness and robustness of agents evolving in environments generated
by POET to agents evolved in handcrafted curricula of environments. Our agents
each contain of a population of individuals being evolved with a genetic
algorithm. This population is called the agent-population. We show that
agent-populations evolving in open-endedly evolving environments exhibit larger
morphological diversity than agent-populations evolving in hand crafted
curricula of environments. POET proved capable of creating a curriculum of
environments which encouraged both diversity and quality in the populations.
This suggests that POET may be capable of reducing premature convergence in
co-optimisation of morphology and controllers.Comment: 17 pages, 8 figure
- …