26 research outputs found
Open-endedness induced through a predator-prey scenario using modular robots
This work investigates how a predator-prey scenario can induce the emergence
of Open-Ended Evolution (OEE). We utilize modular robots of fixed morphologies
whose controllers are subject to evolution. In both species, robots can send
and receive signals and perceive the relative positions of other robots in the
environment. Specifically, we introduce a feature we call a tagging system: it
modifies how individuals can perceive each other and is expected to increase
behavioral complexity. Our results show the emergence of adaptive strategies,
demonstrating the viability of inducing OEE through predator-prey dynamics
using modular robots. Such emergence, nevertheless, seemed to depend on
conditioning reproduction to an explicit behavioral criterion
On the Schedule for Morphological Development of Evolved Modular Soft Robots
Development is fundamental for living beings. As robots are often designed to mimic biological organisms, development is believed to be crucial for achieving successful results in robotic agents, as well. What is not clear, though, is the most appropriate scheduling for development. While in real life systems development happens mostly during the initial growth phase of organisms, it has not yet been investigated whether such assumption holds also for artificial creatures. In this paper, we employ a evolutionary approach to optimize the development—according to different representations—of Voxel-based Soft Robots (VSRs), a kind of modular robots. In our study, development consists in the addition of new voxels to the VSR, at fixed time instants, depending on the development schedule. We experiment with different schedules and show that, similarly to living organisms, artificial agents benefit from development occurring at early stages of life more than from development lasting for their entire life
Environmental regulation using Plasticoding for the evolution of robots
Evolutionary robot systems are usually affected by the properties of the
environment indirectly through selection. In this paper, we present and
investigate a system where the environment also has a direct effect: through
regulation. We propose a novel robot encoding method where a genotype encodes
multiple possible phenotypes, and the incarnation of a robot depends on the
environmental conditions taking place in a determined moment of its life. This
means that the morphology, controller, and behavior of a robot can change
according to the environment. Importantly, this process of development can
happen at any moment of a robot lifetime, according to its experienced
environmental stimuli. We provide an empirical proof-of-concept, and the
analysis of the experimental results shows that Plasticoding improves
adaptation (task performance) while leading to different evolved morphologies,
controllers, and behaviour.Comment: This paper was submitted to the Frontiers in Robotics and AI journal
on the 22/02/2020, and is still under revie
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
A comparison of controller architectures and learning mechanisms for arbitrary robot morphologies
The main question this paper addresses is: What combination of a robot
controller and a learning method should be used, if the morphology of the
learning robot is not known in advance? Our interest is rooted in the context
of morphologically evolving modular robots, but the question is also relevant
in general, for system designers interested in widely applicable solutions. We
perform an experimental comparison of three controller-and-learner
combinations: one approach where controllers are based on modelling animal
locomotion (Central Pattern Generators, CPG) and the learner is an evolutionary
algorithm, a completely different method using Reinforcement Learning (RL) with
a neural network controller architecture, and a combination `in-between' where
controllers are neural networks and the learner is an evolutionary algorithm.
We apply these three combinations to a test suite of modular robots and compare
their efficacy, efficiency, and robustness. Surprisingly, the usual CPG-based
and RL-based options are outperformed by the in-between combination that is
more robust and efficient than the other two setups
Exploring the costs of phenotypic plasticity for evolvable digital organisms
Phenotypic plasticity is usually defined as a property of individual genotypes to produce different phenotypes when exposed to different environmental conditions. While the benefits of plasticity for adaptation are well established, the costs associated with plasticity remain somewhat obscure. Understanding both why and how these costs arise could help us explain and predict the behavior of living creatures as well as allow the design of more adaptable robotic systems. One of the challenges of conducting such investigations concerns the difficulty of isolating the effects of different types of costs and the lack of control over environmental conditions. The present study addresses these challenges by using virtual worlds (software) to investigate the environmentally regulated phenotypic plasticity of digital organisms. The experimental setup guarantees that potential genetic costs of plasticity are isolated from other plasticity-related costs. Multiple populations of organisms endowed with and without phenotypic plasticity in either the body or the brain are evolved in simulation, and organisms must cope with different environmental conditions. The traits and fitness of the emergent organisms are compared, demonstrating cases in which plasticity is beneficial and cases in which it is neutral. The hypothesis put forward here is that the potential benefits of plasticity might be undermined by the genetic costs related to plasticity itself. The results suggest that this hypothesis is true, while further research is needed to guarantee that the observed effects unequivocally derive from genetic costs and not from some other (unforeseen) mechanism related to plasticity.</p