Tag Archives: Evolutionary Robotics

Learning robot simulators

Grant W. Woodford, Mathys C. du Plessis, Bootstrapped Neuro-Simulation for complex robots, . Robotics and Autonomous Systems, Volume 136, 2021 DOI: 10.1016/j.robot.2020.103708.

Robotic simulators are often used to speed up the Evolutionary Robotics (ER) process. Most simulation approaches are based on physics modelling. However, physics-based simulators can become complex to develop and require prior knowledge of the robotic system. Robotics simulators can be constructed using Machine Learning techniques, such as Artificial Neural Networks (ANNs). ANN-based simulator development usually requires a lengthy behavioural data collection period before the simulator can be trained and used to evaluate controllers during the ER process. The Bootstrapped Neuro-Simulation (BNS) approach can be used to simultaneously collect behavioural data, train an ANN-based simulator and evolve controllers for a particular robotic problem. This paper investigates proposed improvements to the BNS approach and demonstrates the viability of the approach by optimising gait controllers for a Hexapod and Snake robot platform.

Chaos theory for modeling behavior of mobile robots that solve tasks evolutionarily

Federico Da Rold, Chaotic analysis of embodied and situated agents, Robotics and Autonomous Systems, Volume 95, 2017, Pages 143-159, DOI: 10.1016/j.robot.2017.06.004.

Embodied and situated view of cognition is a transdisciplinary framework which stresses the importance of real time and dynamical interaction of an agent with the surrounding environment. This article presents a series of evolutionary robotics experiments that operationalize such concept, training miniature two-wheeled mobile robots to autonomously solve a temporal task. In order to provide a numerical description of the robots’ behavior, chaotic measures are estimated on the attractor reconstructed from the recorded positions of the agent. Chaos theory provides a rigorous mathematical framework consistent with an antireductionist approach, useful for understanding embodied and situated systems while avoiding a decomposition of the integrated system brain–body–environment. Time series are analyzed in detail using nonlinear mathematical tools in order to verify the presence of low-dimensional deterministic dynamical systems, a fundamental prerequisite for chaos theory. In particular, the recorded time series are evaluated with nonlinear prediction error to unveil deterministic dynamics, cross-prediction error to determine the stationarity of the signal, and surrogate data testing to verify the existence of nonlinear components in the underlying system. Estimators for quantifying level of chaos and fractal dimension are applied to suitable datasets. Results show that robots governed by a chaotic dynamic are more efficient at adapting to environments never experience during evolution, demonstrating robustness towards novel and unpredictable situations. Furthermore, chaotic measures, in particular fractal dimension, are correlated with the performance if robots exhibit a similar behavioral strategy.