Speeding up Learning with Dynamic Environment Shaping in Evolutionary Robotics

Evolutionary Robotics is a promising approach to automatically build efficient controllers using stochastic optimization techniques. However, works in this area are often confronted to complex environments where even simple tasks cannot be achieved. In the scope of this paper, we propose an approach based on explicit problem decomposition and dynamic environment shaping to ease the learning task

Roborobo! a Fast Robot Simulator for Swarm and Collective Robotics

Roborobo! is a multi-platform, highly portable, robot simulator for large-scale collective robotics experiments. Roborobo! is coded in C++, and follows the KISS guideline ("Keep it simple"). Therefore, its external dependency is solely limited to the widely available SDL library for fast 2D Graphics. Roborobo! is based on a Khepera/ePuck model. It is targeted for fast single and multi-robots simulation, and has already been used in more than a dozen published research mainly concerned with evolutionary swarm robotics, including environment-driven self-adaptation and distributed evolutionary optimization, as well as online onboard embodied evolution and embodied morphogenesis.Comment: 2 pages, 1 figur

Evolutionary robotics : anticipation and the reality gap

Evolutionary Robotics provide efficient tools and approach to address automatic design of controllers for automous mobile robots. However, the computational cost of the optimization process makes it difficult to evolve controllers directly into the real world. This paper addresses the key problem of tranferring into the real world a robotic controller that has been evolved in a robotic simulator. The approach presented here relies on the definition of an anticipation-enabled control architecture. The anticipation module is able to build a partial model of the simulated environment and, once in the real world, performs an error estimation of this model. This error can be reused so as to perform in-situ on-line adaptation of robot control. Experiments in simulation and real-world showed that an evolved robot is able to perform on-line recovery from several kind of locomotion perturbations

Using Echo State Networks for Robot Navigation Behavior Acquisition

International audienceRobot Behavior Learning by Demonstration deals with the ability for a robot to learn a behavior from one or several demonstrations provided by a human teacher, possibly through tele-operation or imitation. This implies controllers that can address both (1) the feature selection problem related to a great amount of mostly irrelevant sensory data and (2) dealing with temporal sequences of demonstrations. Echo State Networks have been proposed recently for time series prediction and have been shown to perform remarkably well on this kind of data. In this paper, we introduce ESN to robot behavior acquisition in the scope of a mobile robot performing navigation tasks. ESN actually show comparable and even better performance with that of other algorithms from the literature in similar experimental conditions. Moreover, some properties regarding dynamics of ESN in the context of learning by demonstration are investigated

Embedded Evolutionary Robotics: The (1+1)-Restart-Online Adaptation Algorithm

International audienceThis paper deals with online onboard behavior optimization for an autonomous mobile robot in the scope of the European FP7 Symbrion Project. The work presented here extends the (1+1)-online algorithm introduced in earlier publication. This algorithm is a variation of a famous Evolution Strategies adapted to autonomous robots. In this paper, we address a limitation of this algorithm regarding the ability to perform global search whenever a local optimum is reached. A new implementation of the algorithm, termed (1+1)-restart-online algorithm, is described and implemented within the Symbrion robotic Cortex M3 microcontroller. Results from the experiments show that the new algorithm is able to escape local optima and, as a consequence, converge faster and provides a richer set of relevant controllers

Unsupervised Learning of Echo State Networks: A case study in Artificial Embryogeny.

International audienceEcho State Networks (ESN) have demonstrated their efficiency in supervised learning of time series: a "reservoir" of neurons provide a set of dynamical systems that can be linearly combined to match the target dynamics, using a simple quadratic optimisation algorithm to tune the few free parameters. In an unsupervised learning context, however, another optimiser is needed. In this paper, an adaptive (1+1)-Evolution Strategy as well as the state-of-the-art CMA-ES are used to optimise an ESN to tackle the "flag" problem, a classical benchmark from multi-cellular artificial embryogeny: the genotype is the cell controller of a Continuous Cellular Automata, and the phenotype, the image that corresponds to the fixed point of the resulting dynamical system, must match a given 2D pattern. This approach is able to provide excellent results with few evaluations, and favourably compares to that using the NEAT algorithm (a state-of-the-art neuro-evolution method) to evolve the cell controllers. Some characteristics of the fitness landscape of the ESN-based method are also investigated

Memory-Enhanced Evolutionary Robotics: The Echo State Network Approach

International audienceInterested in Evolutionary Robotics, this paper focuses on the acquisition and exploitation of memory skills. The targeted task is a well-studied benchmark problem, the Tolman maze, requiring in principle the robotic controller to feature some (limited) counting abilities. An elaborate experimental setting is used to enforce the controller generality and prevent opportunistic evolution from mimicking deliberative skills through smart reactive heuristics. The paper compares the prominent NEAT approach, achieving the non-parametric optimization of Neural Nets, with the evolutionary optimization of Echo State Networks, pertaining to the recent field of Reservoir Computing. While both search spaces offer a sufficient expressivity and enable the modelling of complex dynamic systems, the latter one is amenable to robust parametric, linear optimization with Covariance Matrix Adaptation-Evolution Strategies