130 research outputs found
Lamarckian Evolution of Simulated Modular Robots
We study evolutionary robot systems where not only the robot brains but also the robot bodies are evolvable. Such systems need to include a learning period right after ‘birth' to acquire a controller that fits the newly created body. In this paper we investigate the possibility of bootstrapping infant robot learning through employing Lamarckian inheritance of parental controllers. In our system controllers are encoded by a combination of a morphology dependent component, a Central Pattern Generator (CPG), and a morphology independent part, a Compositional Pattern Producing Network (CPPN). This makes it possible to transfer the CPPN part of controllers between different morphologies and to create a Lamarckian system. We conduct experiments with simulated modular robots whose fitness is determined by the speed of locomotion, establish the benefits of inheriting optimized parental controllers, shed light on the conditions that influence these benefits, and observe that changing the way controllers are evolved also impacts the evolved morphologies
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
Acquiring moving skills in robots with evolvable morphologies: Recent results and outlook
© 2017 ACM. We construct and investigate a strongly embodied evolutionary system, where not only the controllers but also the morphologies undergo evolution in an on-line fashion. In these studies, we have been using various types of robot morphologies and controller architectures in combination with several learning algorithms, e.g. evolutionary algorithms, reinforcement learning, simulated annealing, and HyperNEAT. This hands-on experience provides insights and helps us elaborate on interesting research directions for future development
A Comparative Study of Brain Reproduction Methods for Morphologically Evolving Robots
In the most extensive robot evolution systems, both the bodies and the brains
of the robots undergo evolution and the brains of 'infant' robots are also
optimized by a learning process immediately after 'birth'. This paper is
concerned with the brain evolution mechanism in such a system. In particular,
we compare four options obtained by combining asexual or sexual brain
reproduction with Darwinian or Lamarckian evolution mechanisms. We conduct
experiments in simulation with a system of evolvable modular robots on two
different tasks. The results show that sexual reproduction of the robots'
brains is preferable in the Darwinian framework, but the effect is the opposite
in the Lamarckian system (both using the same infant learning method). Our
experiments suggest that the overall best option is asexual reproduction
combined with the Lamarckian framework, as it obtains better robots in terms of
fitness than the other three. Considering the evolved morphologies, the
different brain reproduction methods do not lead to differences. This result
indicates that the morphology of the robot is mainly determined by the task and
the environment, not by the brain reproduction methods.Comment: 8 pages, ALif
Analysing the relative importance of robot brains and bodies
The evolution of robots, when applied to both the morphologies and the controllers, is not only a means to obtain high-quality robot designs, but also a process that results in many body-brain-fitness data points. Inspired by this perspective, in this paper we investigate the relative importance of robot bodies and brains for a good fitness. We introduce a method to isolate and quantify the effect of the bodies and brains on the quality of the robots and perform a case study. The method is general in that it is not restricted to evolutionary systems. For the case study, we use a system of modular robots, where the bodies are evolvable and the brains are evolvable and learnable. These case studies validate the usefulness of our method and deliver interesting insights into the interplay between bodies and brains in evolutionary robotics
If it evolves it needs to learn
We elaborate on (future) evolutionary robot systems where morphologies and controllers of real robots are evolved in the real-world. We argue that such systems must contain a learning component where a newborn robot refines its inherited controller to align with its body, which will inevitably be different from its parents
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