256 research outputs found
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
Linear combination of one-step predictive information with an external reward in an episodic policy gradient setting: a critical analysis
One of the main challenges in the field of embodied artificial intelligence
is the open-ended autonomous learning of complex behaviours. Our approach is to
use task-independent, information-driven intrinsic motivation(s) to support
task-dependent learning. The work presented here is a preliminary step in which
we investigate the predictive information (the mutual information of the past
and future of the sensor stream) as an intrinsic drive, ideally supporting any
kind of task acquisition. Previous experiments have shown that the predictive
information (PI) is a good candidate to support autonomous, open-ended learning
of complex behaviours, because a maximisation of the PI corresponds to an
exploration of morphology- and environment-dependent behavioural regularities.
The idea is that these regularities can then be exploited in order to solve any
given task. Three different experiments are presented and their results lead to
the conclusion that the linear combination of the one-step PI with an external
reward function is not generally recommended in an episodic policy gradient
setting. Only for hard tasks a great speed-up can be achieved at the cost of an
asymptotic performance lost
Fast Damage Recovery in Robotics with the T-Resilience Algorithm
Damage recovery is critical for autonomous robots that need to operate for a
long time without assistance. Most current methods are complex and costly
because they require anticipating each potential damage in order to have a
contingency plan ready. As an alternative, we introduce the T-resilience
algorithm, a new algorithm that allows robots to quickly and autonomously
discover compensatory behaviors in unanticipated situations. This algorithm
equips the robot with a self-model and discovers new behaviors by learning to
avoid those that perform differently in the self-model and in reality. Our
algorithm thus does not identify the damaged parts but it implicitly searches
for efficient behaviors that do not use them. We evaluate the T-Resilience
algorithm on a hexapod robot that needs to adapt to leg removal, broken legs
and motor failures; we compare it to stochastic local search, policy gradient
and the self-modeling algorithm proposed by Bongard et al. The behavior of the
robot is assessed on-board thanks to a RGB-D sensor and a SLAM algorithm. Using
only 25 tests on the robot and an overall running time of 20 minutes,
T-Resilience consistently leads to substantially better results than the other
approaches
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
Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm
The recently introduced Multi-dimensional Archive of Phenotypic Elites
(MAP-Elites) is an evolutionary algorithm capable of producing a large archive
of diverse, high-performing solutions in a single run. It works by discretizing
a continuous feature space into unique regions according to the desired
discretization per dimension. While simple, this algorithm has a main drawback:
it cannot scale to high-dimensional feature spaces since the number of regions
increase exponentially with the number of dimensions. In this paper, we address
this limitation by introducing a simple extension of MAP-Elites that has a
constant, pre-defined number of regions irrespective of the dimensionality of
the feature space. Our main insight is that methods from computational geometry
could partition a high-dimensional space into well-spread geometric regions. In
particular, our algorithm uses a centroidal Voronoi tessellation (CVT) to
divide the feature space into a desired number of regions; it then places every
generated individual in its closest region, replacing a less fit one if the
region is already occupied. We demonstrate the effectiveness of the new
"CVT-MAP-Elites" algorithm in high-dimensional feature spaces through
comparisons against MAP-Elites in maze navigation and hexapod locomotion tasks
On the Evolutionary Co-Adaptation of Morphology and Distributed Neural Controllers in Adaptive Agents
The attempt to evolve complete embodied and situated artificial creatures in which
both morphological and control characteristics are adapted during the evolutionary
process has been and still represents a long term goal key for the artificial life and
the evolutionary robotics community.
Loosely inspired by ancient biological organisms which are not provided with a
central nervous system and by simple organisms such as stick insects, this thesis
proposes a new genotype encoding which allows development and evolution of mor-
phology and neural controller in artificial agents provided with a distributed neural
network.
In order to understand if this kind of network is appropriate for the evolution of
non trivial behaviours in artificial agents, two experiments (description and results
will be shown in chapter 3) in which evolution was applied only to the controller’s
parameters were performed.
The results obtained in the first experiment demonstrated how distributed neural
networks can achieve a good level of organization by synchronizing the output of
oscillatory elements exploiting acceleration/deceleration mechanisms based on local
interactions.
In the second experiment few variants on the topology of neural architecture were
introduced. Results showed how this new control system was able to coordinate the
legs of a simulated hexapod robot on two different gaits on the basis of the external
circumstances.
After this preliminary and successful investigation, a new genotype encoding able to
develop and evolve artificial agents with no fixed morphology and with a distributed
neural controller was proposed. A second set of experiments was thus performed
and the results obtained confirmed both the effectiveness of genotype encoding and
the ability of distributed neural network to perform the given task.
The results have also shown the strength of genotype both in generating a wide
range of different morphological structures and in favouring a direct co-adaptation
between neural controller and morphology during the evolutionary process.
Furthermore the simplicity of the proposed model has showed the effective role of
specific elements in evolutionary experiments. In particular it has demonstrated the
importance of the environment and its complexity in evolving non-trivial behaviours
and also how adding an independent component to the fitness function could help
the evolutionary process exploring a larger space solutions avoiding a premature
convergence towards suboptimal solutions
Robots that can adapt like animals
As robots leave the controlled environments of factories to autonomously
function in more complex, natural environments, they will have to respond to
the inevitable fact that they will become damaged. However, while animals can
quickly adapt to a wide variety of injuries, current robots cannot "think
outside the box" to find a compensatory behavior when damaged: they are limited
to their pre-specified self-sensing abilities, can diagnose only anticipated
failure modes, and require a pre-programmed contingency plan for every type of
potential damage, an impracticality for complex robots. Here we introduce an
intelligent trial and error algorithm that allows robots to adapt to damage in
less than two minutes, without requiring self-diagnosis or pre-specified
contingency plans. Before deployment, a robot exploits a novel algorithm to
create a detailed map of the space of high-performing behaviors: This map
represents the robot's intuitions about what behaviors it can perform and their
value. If the robot is damaged, it uses these intuitions to guide a
trial-and-error learning algorithm that conducts intelligent experiments to
rapidly discover a compensatory behavior that works in spite of the damage.
Experiments reveal successful adaptations for a legged robot injured in five
different ways, including damaged, broken, and missing legs, and for a robotic
arm with joints broken in 14 different ways. This new technique will enable
more robust, effective, autonomous robots, and suggests principles that animals
may use to adapt to injury
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