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 si...

We present the results of a research aimed at improving the Q-learning method through the use of artificial neural networks. Neural implementations are interesting due to their generalisation ability. Two implementations are proposed: one with a competitive multilayer perceptron and the other with a self-organising map. Results obtained on a task of learning an obstacle avoidance behaviour for ...

Behavior-based artificial intelligent system is to derive the complicated behaviors by selecting appropriate one from a set of basic behaviors. Many robot systems have used behavior-based systems since the 1980’s. In this paper, we propose new method to create the sequences of behaviors appropriate to the changing environments by adding the function of learning with Learning Classifier System t...

The design of behavior generating control structures for real robots acting autonomously in a real and changing environment is a complex task. This is in particular true with respect to the debugging process, the documentation of the encountered behavior, its quantitative analysis and the final evaluation. To successfully implement such a behavior, it is vital to couple the synthesis on a simul...

CAM-Brain is the model to create neural networks based on cellular automata, and ®nally aims at developing an arti®cial brain. In particular, this model can rapidly evolve the neural networks composed of several thousand modules with special purpose computers such as CAM-8 at MIT and CBM at ATR. This paper attempts to evolve a module of CAM-Brain for the problem to control a mobile robot, espec...

We describe a hippocampal neural model in which spatio-temporal features of the environment are extracted by visually driven neu-rons. The neuronal ring activity implicitly measures properties like agent-landmark distance and egocentric orientation to visual cues. This leads to a neural representation where populations of place cells encode spatial locations within the environment. In addition,...

We present a biologically motivated computational model that is able to anticipate and evaluate multiple hypothetical sensorimotor sequences. Our Model for Anticipation based on Cortical Representations (MACOR) allows a completely parallel search at the neocortical level using assemblies of rate coded neurons for grouping, separation, and selection of sensorimotor sequences. For a vision-contro...

We propose a bio-inspired approach to autonomous navigation based on some of the components that rats use for navigation. A spatial model of the environment is constructed by unsupervised Hebbian learning. The representation consists of a population of localized overlapping place elds, modeling place cell activity in the rat Hippocampus. Place elds are established by extracting spatio-temporal ...

This work addresses the real time control of the Khepera mobile robot [1] navigation in a maze with reflector walls. Boolean Neural Networks such as RAM [2] and GSN [3] models are applied to drive the vehicle, following a light source, while avoiding obstacles. Both neural networks are implemented with simple logic and arithmetic functions (NOT, AND, OR, Addition, and Comparison), aiming to imp...

The pitfalls of naive robot simulations have been recognised for areas such as evolutionary robotics. It has been suggested that carefully validated ispell slides.tex simulations with a proper treatment of noise may overcome these problems. This paper reports the results of experiments intended to test some of these claims. A simulation was constructed of a two-wheeled Khepera robot with IR and...