3,130 research outputs found
Combating catastrophic forgetting with developmental compression
Generally intelligent agents exhibit successful behavior across problems in
several settings. Endemic in approaches to realize such intelligence in
machines is catastrophic forgetting: sequential learning corrupts knowledge
obtained earlier in the sequence, or tasks antagonistically compete for system
resources. Methods for obviating catastrophic forgetting have sought to
identify and preserve features of the system necessary to solve one problem
when learning to solve another, or to enforce modularity such that minimally
overlapping sub-functions contain task specific knowledge. While successful,
both approaches scale poorly because they require larger architectures as the
number of training instances grows, causing different parts of the system to
specialize for separate subsets of the data. Here we present a method for
addressing catastrophic forgetting called developmental compression. It
exploits the mild impacts of developmental mutations to lessen adverse changes
to previously-evolved capabilities and `compresses' specialized neural networks
into a generalized one. In the absence of domain knowledge, developmental
compression produces systems that avoid overt specialization, alleviating the
need to engineer a bespoke system for every task permutation and suggesting
better scalability than existing approaches. We validate this method on a robot
control problem and hope to extend this approach to other machine learning
domains in the future
Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better
Evolutionary robot systems offer two principal advantages: an advanced way of
developing robots through evolutionary optimization and a special research
platform to conduct what-if experiments regarding questions about evolution.
Our study sits at the intersection of these. We investigate the question ``What
if the 18th-century biologist Lamarck was not completely wrong and individual
traits learned during a lifetime could be passed on to offspring through
inheritance?'' We research this issue through simulations with an evolutionary
robot framework where morphologies (bodies) and controllers (brains) of robots
are evolvable and robots also can improve their controllers through learning
during their lifetime. Within this framework, we compare a Lamarckian system,
where learned bits of the brain are inheritable, with a Darwinian system, where
they are not. Analyzing simulations based on these systems, we obtain new
insights about Lamarckian evolution dynamics and the interaction between
evolution and learning. Specifically, we show that Lamarckism amplifies the
emergence of `morphological intelligence', the ability of a given robot body to
acquire a good brain by learning, and identify the source of this success:
`newborn' robots have a higher fitness because their inherited brains match
their bodies better than those in a Darwinian system.Comment: preprint-nature scientific report. arXiv admin note: text overlap
with arXiv:2303.1259
Exploring the Modularity and Structure of Robots Evolved in Multiple Environments
Traditional techniques for the design of robots require human engineers to plan every aspect of the system, from body to controller. In contrast, the field of evolu- tionary robotics uses evolutionary algorithms to create optimized morphologies and neural controllers with minimal human intervention. In order to expand the capability of an evolved agent, it must be exposed to a variety of conditions and environments.
This thesis investigates the design and benefits of virtual robots which can reflect the structure and modularity in the world around them. I show that when a robot’s morphology and controller enable it to perceive each environment as a collection of independent components, rather than a monolithic entity, evolution only needs to optimize on a subset of environments in order to maintain performance in the overall larger environmental space. I explore previously unused methods in evolutionary robotics to aid in the evolution of modularity, including using morphological and neurological cost.
I utilize a tree morphology which makes my results generalizable to other mor- phologies while also allowing in depth theoretical analysis about the properties rel- evant to modularity in embodied agents. In order to better frame the question of modularity in an embodied context, I provide novel definitions of morphological and neurological modularity as well as create the sub-goal interference metric which mea- sures how much independence a robot exhibits with regards to environmental stimu- lus.
My work extends beyond evolutionary robotics and can be applied to the opti- mization of embodied systems in general as well as provides insight into the evolution of form in biological organisms
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
Hardware Design for Autonomous Robot Evolution
The long term goal of the Autonomous Robot Evolution (ARE) project is to create populations of physical robots, in which both the controllers and body plans are evolved. The transition for evolutionary designs from purely simulation environments into the real world creates the possibility for new types of system able to adapt to unknown and changing environments. In this paper, a system for creating robots is introduced in order to allow for their body plans to be designed algorithmically and physically instantiated using the previously introduced Robot Fabricator. This system consists of two types of components. Firstly, \textit{skeleton} parts are created bespoke for each design by 3D printing, allowing the overall shape of the robot to include almost infinite variety. To allow for the shortcomings of 3D printing, the second type of component are \textit{organs} which contain components such as motors and sensors, and can be attached to the skeleton to provide particular functions. Specific organ designs are presented, with discussion of the design challenges for evolutionary robotics in hardware. The Robot Fabricator is extended to allow for robots with joints, and some example body plans shown to demonstrate the diversity possible using this system of robot generation
Evolving soft locomotion in aquatic and terrestrial environments: effects of material properties and environmental transitions
Designing soft robots poses considerable challenges: automated design
approaches may be particularly appealing in this field, as they promise to
optimize complex multi-material machines with very little or no human
intervention. Evolutionary soft robotics is concerned with the application of
optimization algorithms inspired by natural evolution in order to let soft
robots (both morphologies and controllers) spontaneously evolve within
physically-realistic simulated environments, figuring out how to satisfy a set
of objectives defined by human designers. In this paper a powerful evolutionary
system is put in place in order to perform a broad investigation on the
free-form evolution of walking and swimming soft robots in different
environments. Three sets of experiments are reported, tackling different
aspects of the evolution of soft locomotion. The first two sets explore the
effects of different material properties on the evolution of terrestrial and
aquatic soft locomotion: particularly, we show how different materials lead to
the evolution of different morphologies, behaviors, and energy-performance
tradeoffs. It is found that within our simplified physics world stiffer robots
evolve more sophisticated and effective gaits and morphologies on land, while
softer ones tend to perform better in water. The third set of experiments
starts investigating the effect and potential benefits of major environmental
transitions (land - water) during evolution. Results provide interesting
morphological exaptation phenomena, and point out a potential asymmetry between
land-water and water-land transitions: while the first type of transition
appears to be detrimental, the second one seems to have some beneficial
effects.Comment: 37 pages, 22 figures, currently under review (journal
The Watchmaker's guide to Artificial Life: On the Role of Death, Modularity and Physicality in Evolutionary Robotics
Photograph used for a newspaper owned by the Oklahoma Publishing Company
Open-ended Search through Minimal Criterion Coevolution
Search processes guided by objectives are ubiquitous in machine learning. They iteratively reward artifacts based on their proximity to an optimization target, and terminate upon solution space convergence. Some recent studies take a different approach, capitalizing on the disconnect between mainstream methods in artificial intelligence and the field\u27s biological inspirations. Natural evolution has an unparalleled propensity for generating well-adapted artifacts, but these artifacts are decidedly non-convergent. This new class of non-objective algorithms induce a divergent search by rewarding solutions according to their novelty with respect to prior discoveries. While the diversity of resulting innovations exhibit marked parallels to natural evolution, the methods by which search is driven remain unnatural. In particular, nature has no need to characterize and enforce novelty; rather, it is guided by a single, simple constraint: survive long enough to reproduce. The key insight is that such a constraint, called the minimal criterion, can be harnessed in a coevolutionary context where two populations interact, finding novel ways to satisfy their reproductive constraint with respect to each other. Among the contributions of this dissertation, this approach, called minimal criterion coevolution (MCC), is the primary (1). MCC is initially demonstrated in a maze domain (2) where it evolves increasingly complex mazes and solutions. An enhancement to the initial domain (3) is then introduced, allowing mazes to expand unboundedly and validating MCC\u27s propensity for open-ended discovery. A more natural method of diversity preservation through resource limitation (4) is introduced and shown to maintain population diversity without comparing genetic distance. Finally, MCC is demonstrated in an evolutionary robotics domain (5) where it coevolves increasingly complex bodies with brain controllers to achieve principled locomotion. The overall benefit of these contributions is a novel, general, algorithmic framework for the continual production of open-ended dynamics without the need for a characterization of behavioral novelty
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