Self-Organisation of Neural Topologies by Evolutionary Reinforcement Learning

In this article we present EANT, "Evolutionary Acquisition of Neural Topologies", a method that creates neural networks (NNs) by evolutionary reinforcement learning. The structure of NNs is developed using mutation operators, starting from a minimal structure. Their parameters are optimised using CMA-ES. EANT can create NNs that are very specialised; they achieve a very good performance while being relatively small. This can be seen in experiments where our method competes with a different one, called NEAT, "NeuroEvolution of Augmenting Topologies", to create networks that control a robot in a visual serving scenario

Self-Organisation of Neural Topologies by Evolutionary Reinforcement Learning

In this article we present EANT, "Evolutionary Acquisition of Neural Topologies", a method that creates neural networks (NNs) by evolutionary reinforcement learning. The structure of NNs is developed using mutation operators, starting from a minimal structure. Their parameters are optimised using CMA-ES. EANT can create NNs that are very specialised; they achieve a very good performance while being relatively small. This can be seen in experiments where our method competes with a different one, called NEAT, "NeuroEvolution of Augmenting Topologies", to create networks that control a robot in a visual serving scenario

Combining central pattern generators with the electromagnetism-like algorithm for head motion stabilization during quadruped robot locomotion

Visually-guided locomotion is important for autonomous robotics. However, there are several difficulties, for instance, the head shaking that results from the robot locomotion itself that constraints stable image acquisition and the possibility to rely on that information to act accordingly. In this article, we propose a controller architecture that is able to generate locomotion for a quadruped robot and to generate head motion able to minimize the head motion induced by locomotion itself. The movement controllers are biologically inspired in the concept of Central Pattern Generators (CPGs). CPGs are modelled based on nonlinear dynamical systems, coupled Hopf oscillators. This approach allows to explicitly specify parameters such as amplitude, offset and frequency of movement and to smoothly modulate the generated oscillations according to changes in these parameters. We take advantage of this particularity and propose a combined approach to generate head movement stabilization on a quadruped robot, using CPGs and a global optimization algorithm. The best set of parameters that generates the head movement are computed by the electromagnetism-like algorithm in order to reduce the head shaking caused by locomotion. Experimental results on a simulated AIBO robot demonstrate that the proposed approach generates head movement that does not eliminate but reduces the one induced by locomotion

Fusion of Multiple Tracking Algorithms for Robust People Tracking

This paper shows how the output of a number of detection and tracking algorithms can be fused to achieve robust tracking of people in an indoor environment. The new tracking system contains three co-operating parts: i) an Active Shape Tracker using a PCA-generated model of pedestrian outline shapes, ii) a Region Tracker, featuring region splitting and merging for multiple hypothesis matching, and iii) a Head Detector to aid in the initialisation of tracks. Data from the three parts are fused together to select the best tracking hypotheses. The new method is validated using sequences from surveillance cameras in a underground station. It is demonstrated that robust realtime tracking of people can be achieved with the new tracking system using standard PC hardware

Learning neural networks for visual servoing using evolutionary methods

In this article we introduce a method to learn neural networks that solve a visual servoing task. Our method, called EANT, Evolutionary Acquisition of Neural Topologies, starts from a minimal network structure and gradually develops it further using evolutionary reinforcement learning. We have improved EANT by combining it with an optimisation technique called CMA-ES, Covariance Matrix Adaptation Evolution Strategy. Results from experiments with a 3 DOF visual servoing task show that the new CMA-ES based EANT develops very good networks for visual servoing. Their performance is significantly better than those developed by the original EANT and traditional visual servoing approaches. 1. Introduction and Relate