3,982 research outputs found
Development of Multiple Behaviors in Evolving Robots
We investigate whether standard evolutionary robotics methods can be extended to support the evolution of multiple behaviors by forcing the retention of variations that are adaptive with respect to all required behaviors. This is realized by selecting the individuals located in the first Pareto fronts of the multidimensional fitness space in the case of a standard evolutionary algorithms and by computing and using multiple gradients of the expected fitness in the case of a modern evolutionary strategies that move the population in the direction of the gradient of the fitness. The results collected on two extended versions of state-of-the-art benchmarking problems indicate that the latter method permits to evolve robots capable of producing the required multiple behaviors in the majority of the replications and produces significantly better results than all the other methods considered
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents
As discussed in previous studies, the efficacy of evolutionary or
reinforcement learning algorithms for continuous control optimization can be
enhanced by including a neural module dedicated to feature extraction trained
through self-supervised methods. In this paper we report additional experiments
supporting this hypothesis and we demonstrate how the advantage provided by
feature extraction is not limited to problems that benefit from dimensionality
reduction or that involve agents operating on the basis of allocentric
perception. We introduce a method that permits to continue the training of the
feature-extraction module during the training of the policy network and that
increases the efficacy of feature extraction. Finally, we compare alternative
feature-extracting methods and we show that sequence-to-sequence learning
yields better results than the methods considered in previous studies
On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation
Biological and robotic grasp and manipulation are undeniably similar at the
level of mechanical task performance. However, their underlying fundamental
biological vs. engineering mechanisms are, by definition, dramatically
different and can even be antithetical. Even our approach to each is
diametrically opposite: inductive science for the study of biological systems
vs. engineering synthesis for the design and construction of robotic systems.
The past 20 years have seen several conceptual advances in both fields and the
quest to unify them. Chief among them is the reluctant recognition that their
underlying fundamental mechanisms may actually share limited common ground,
while exhibiting many fundamental differences. This recognition is particularly
liberating because it allows us to resolve and move beyond multiple paradoxes
and contradictions that arose from the initial reasonable assumption of a large
common ground. Here, we begin by introducing the perspective of neuromechanics,
which emphasizes that real-world behavior emerges from the intimate
interactions among the physical structure of the system, the mechanical
requirements of a task, the feasible neural control actions to produce it, and
the ability of the neuromuscular system to adapt through interactions with the
environment. This allows us to articulate a succinct overview of a few salient
conceptual paradoxes and contradictions regarding under-determined vs.
over-determined mechanics, under- vs. over-actuated control, prescribed vs.
emergent function, learning vs. implementation vs. adaptation, prescriptive vs.
descriptive synergies, and optimal vs. habitual performance. We conclude by
presenting open questions and suggesting directions for future research. We
hope this frank assessment of the state-of-the-art will encourage and guide
these communities to continue to interact and make progress in these important
areas
Evolving generalist controllers to handle a wide range of morphological variations
Neuro-evolutionary methods have proven effective in addressing a wide range
of tasks. However, the study of the robustness and generalisability of evolved
artificial neural networks (ANNs) has remained limited. This has immense
implications in the fields like robotics where such controllers are used in
control tasks. Unexpected morphological or environmental changes during
operation can risk failure if the ANN controllers are unable to handle these
changes. This paper proposes an algorithm that aims to enhance the robustness
and generalisability of the controllers. This is achieved by introducing
morphological variations during the evolutionary process. As a results, it is
possible to discover generalist controllers that can handle a wide range of
morphological variations sufficiently without the need of the information
regarding their morphologies or adaptation of their parameters. We perform an
extensive experimental analysis on simulation that demonstrates the trade-off
between specialist and generalist controllers. The results show that
generalists are able to control a range of morphological variations with a cost
of underperforming on a specific morphology relative to a specialist. This
research contributes to the field by addressing the limited understanding of
robustness and generalisability in neuro-evolutionary methods and proposes a
method by which to improve these properties
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