On the Emergence of Biologically-Plausible Representation in Recurrent Neural Networks for Navigation

Biologically plausible representations have been found to emerge in particular recurrent neural networks when training on path-integration [1, 2]. This report explores factors influencing the occurrence of entorhinal-like representations in recurrent neural networks. Reproducing simplified models from existing studies and created a hybrid model to explore additional factors, including the input features, structural properties, and regularization techniques in recurrent neural networks. Additional experiments evaluate the difference in training performance when entorhinal-like representations are introduced to a recurrent neural network. This report also assesses existing and experimental visualization techniques in their ability to visualize the performance and representation of recurrent neurons. While some experiments show specialized representations, mostly due to regularization; none of the experiments showed typical entorhinal-like representation. These results show how sensitive the emergence of biologically-plausible representations is to network conditions and training procedure,casting some doubt on the generality of the conclusions proposed in earlier work.Computer Scienc

ci-group/revolve2: 1.0.0rc1

<p><strong>New features</strong></p> <ul> <li>Reworked interface for simulation and modular robot simulation.</li> <li>Added feedback for brains. Implemented for active hinge positions.</li> <li>Added v2 hardware support in simulation.</li> <li>Armature, kv, and kp can now be set directly in ActiveHinge.</li> <li>RPi controller has been reworked to be a lot easier to use. Remote has been removed temporarily but we will be added again soon in better shape.</li> </ul> <p><strong>Documentation and general project things</strong></p> <ul> <li>Many and various improvements to documentation.</li> <li><ul> <li>Various improvements and fixes to the CI.</li> </ul> </li> <li><ul> <li>Improved and added examples.</li> </ul> </li> <li><ul> <li>Updated project layout and pyproject.toml files.</li> </ul> </li> </ul> <p><strong>Bug fixes and minor updates</strong></p> <ul> <li>Fixed bug in geometries where colors were not properly initialized.</li> <li>Added Vector2d and replaced Vector3d wherever applicable.</li> <li>Removed warning message for NamedTemporaryFile on Windows, as the applied backup strategy turns out to be reliable, making the * Various minor refactors and cleanups.message superfluous.</li> </ul> <p><strong>Regressions</strong> RPi controller remote has been removed, but will be added again in the next version in an improved form.</p&gt

Impact of energy efficiency on the morphology and behaviour of evolved robots

Most evolutionary robotics studies focus on evolving some targeted behavior without considering energy usage. In this paper, we extend our simulator with a battery model to take energy consumption into account in a system where robot morphologies and controllers evolve simultaneously. The results show that including the energy consumption in the fitness in a multi-objective fashion reduces the average size of robot bodies while reducing their speed. However, robots generated without size reduction can achieve speeds comparable to robots from the baseline set

Co-optimizing for task performance and energy efficiency in evolvable robots

Evolutionary robotics is concerned with optimizing autonomous robots for one or more specific tasks. Remarkably, the energy needed to operate autonomously is hardly ever considered. This is quite striking because energy consumption is a crucial factor in real-world applications and ignoring this aspect can increase the reality gap. In this paper, we aim to mitigate this problem by extending our robot simulator framework with a model of a battery module and studying its effect on robot evolution. The key idea is to include energy efficiency in the definition of fitness. The robots will need to evolve to achieve high gait speed and low energy consumption. Since our system evolves the robots’ morphologies as well as their controllers, we investigate the effect of the energy extension on the morphologies and on the behavior of the evolved robots. The results show that by including the energy consumption, the evolution is not only able to achieve higher task performance (robot speed), but it reaches good performance faster. Inspecting the evolved robots and their behaviors discloses that these improvements are not only caused by better morphologies, but also by better settings of the robots’ controller parameters

Robotic task affects the resulting morphology and behaviour in evolutionary robotics

In evolution, the evolutionary success of individuals is influenced in equal parts by the environment they are living in and by the adapting capability that they possess. In this work, instead of analyzing the adaptability of a population, we investigate how different environments influence the evolution of morphologies and controllers of modular robots. Previous work analyzed how robot evolution is influenced by different environments while keeping fixed the robot task. However, each environment may present different tasks that influence robot success. In this work, we investigate how different tasks in the same physical environment influence the evolutionary outcome. Specifically, we compare morphologies and behaviors under two tasks, undirected locomotion, and rotation in a flat terrain environment. We perform this analysis by analyzing the morphological and behavioral traits of the final population. Results suggest that both morphology and behavior types are strongly influenced by the environment, with the rotation task producing more proportionate and balanced robots compared to the locomotion task

The Influence of Robot Traits and Evolutionary Dynamics on the Reality Gap

The elephant in the room for evolutionary robotics is the reality gap. In the history of the field, several studies investigated this phenomenon on fixed robot morphologies where only the controllers evolved. This paper addresses the reality gap in a wider context, in a system where both morphologies and controllers evolve. In this context the morphology of the robots becomes a variable with a currently unknown influence. To examine this influence, we construct a test suite of robots with various morphologies and evolve their controllers for an effective gait. Comparing the simulated and the real-world performance of evolved controllers sampled at different generations during the evolutionary process, we gain new insights into the factors that influence the reality gap

Influences of Artificial Speciation on Morphological Robot Evolution

One key challenge in Evolutionary Robotics (ER) is to evolve morphology and controllers of robots. Most experiments in the field converge rapidly to a single solution for the entire population. Early convergence results in a premature loss of diversity, which creates inconsistent results across multiple runs, sometimes converging to a local optimum. In Nature we can observe the opposite behavior: the more time passes, the more life becomes increasingly diverse. The increasing diversity is correlated to the formation of new species, which is catalyzed by reproductive isolation caused by physical or behavioral separation. Inspired by natural evolution, in this paper we apply artificial speciation based on morphological traits to an ER system. Individuals are forced to crossover only with individuals within the same species and a protection mechanism is applied to newly created species. In our experiments, we demonstrate that this speciation mechanism, inspired by NEAT, can evolve a population rich of many coexisting individuals, differing both in morphology and behavior