Robust Multi-Cellular Developmental Design

This paper introduces a continuous model for Multi-cellular Developmental Design. The cells are fixed on a 2D grid and exchange "chemicals" with their neighbors during the growth process. The quantity of chemicals that a cell produces, as well as the differentiation value of the cell in the phenotype, are controlled by a Neural Network (the genotype) that takes as inputs the chemicals produced by the neighboring cells at the previous time step. In the proposed model, the number of iterations of the growth process is not pre-determined, but emerges during evolution: only organisms for which the growth process stabilizes give a phenotype (the stable state), others are declared nonviable. The optimization of the controller is done using the NEAT algorithm, that optimizes both the topology and the weights of the Neural Networks. Though each cell only receives local information from its neighbors, the experimental results of the proposed approach on the 'flags' problems (the phenotype must match a given 2D pattern) are almost as good as those of a direct regression approach using the same model with global information. Moreover, the resulting multi-cellular organisms exhibit almost perfect self-healing characteristics

Apprentissage d'une représentation humaine du monde par un robot

National audienc

Apprentissage d'une représentation humaine du monde par un robot

National audienc

From distributed robot perception to human topology: a learning model

International audienceThis poster presents an approach to enable autonomous mobile robots to link perceived information from their environment to names of places (toponyms acquired through interaction with human beings)

Wrapper for Object Detection in an Autonomous Mobile Robot

International audienceIn this paper, we address the problem of object detection in an autonomous mobile robot. The goal is to define a perceptual system that can quickly adapt itself to detect a specific object. To achieve this, we propose a goal-oriented approach to let the robot build the most fitted image descriptors for a given object

Abstracting Visual Percepts to learn Concepts

International audienceTo efficiently identify properties from its environment is an essential ability of a mobile robot who needs to interact with humans. Successful approaches to provide robots with such ability are based on ad-hoc perceptual representation provided by AI designers. Instead, our goal is to endow autonomous mobile robots (in our experiments a Pioneer 2DX) with a perceptual system that can efficiently adapt itself to ease the learning task required to anchor symbols. Our approach is in the line of meta-learning algorithms that iteratively change representations so as to discover one that is well fitted for the task. The architecture we propose may be seen as a combination of the two widely used approach in feature selection: the Wrapper-model and the Filter-model. Experiments using the PLIC system to identify the presence of Humans and Fire Extinguishers show the interest of such an approach, which dynamically abstracts a well fitted image description depending on the concept to learn

Combining wrapper and filter approaches for learning concepts from images provided by a mobile robot.

International audienc

Combining wrapper and filter approaches for learning concepts from images provided by a mobile robot.

International audienc

A Wrapper-Based Approach to Robot Learning Concepts from Images

International audienceThis work is about the building of a lexicon of shared symbols between a Pioneer2DX mobile robot and its human interlocutors. This lexicon contains words corresponding to objects seen in the environment. The difficulty relies in grounding these symbols with the actual data provided by the camera of the robot with respect to the learning scenario shown in figur

A heterogeneous, 3d, multiscale representation of the soil architecture to model microbial and faunal processes

Biological and physical soil processes show a high degree of interactions across scales. For example, earthworm bioturbation at the soil profile scale acts on the abiotic conditions controlling microbial organic degradation at the pore scale. Bioturbation also influences the general characteristics of the profile like total organic matter content or water conductivity. Technically, Multi-Agents Systems (MAS) are particularly pertinent to reproduce global behavior by modeling autonomous system elements at a micro-level spatial scale. In a MAS paradigm, the soil architecture is considered as the environment with and in which biological and physical agents interact. By architecture, we consider here the spatial repartition of the soil's constituents like pores, minerals, or organic matters. The APSF (Agent Pore Solid Fractal) is a MAS environment that represents a heterogeneous, three-dimensional, and multiscale soil architecture at the profile extent. The APSF has been developed as an efficient and low-computation-cost spatial representation which is mass-balanced for the different considered soil fractions (e.g. mineral size fraction, organic fraction, pore volume). The APSF has been successfully used to simulate earthworm bioturbation and organic matter degradation by microbes in MAS, i.e. within the Sworm and Mior models as well as their coupling. However, the coherence of the APSF in term of pore and aggregate distribution, topology, or connectivity has not been yet assessed whereas their characteristics are crucial in biological and physical soil processes. The aim of this communication is to evaluate the ability of the APSF to realistically represent soil architecture by defining a method of sensitivity analysis to explore the effect of different input parameters on pore and solid spatial distribution. In the APSF, the spatial environment is described as a hierarchy of cubic voxels. For each hierarchical level, the spatial repartition of the different soil components (mineral matter, organic matter, pore) results from the replication of a unique pattern of voxels in a pseudo-fractal mode. The APSF input parameters are, for each level, the proportions of mineral, organic and porous voxels to be found in the construction pattern as well as a coordination index defining the number of neighboring pore voxels. We are currently implementing in the APSF code new chosen output variables to describe the topology of pores and aggregates like connectivity index, autocorrelation function, or chord distribution. We implemented an original method of multidimensional sensitivity analysis to take into account the strong dependence in the input parameters as the sum of voxel proportions strictly equals 1 at a given level. Those proportions are represented as a Dirichlet distribution. The sensitivity analysis has been successfully performed on global non topological outputs like size distribution. The results show that the behavior of the APSF is coherent with its algorithm: the variables of the larger scale levels are the most influent inputs. We still have to explore the sensitivity of the new topological outputs and to compare it with data issued from measurements on real soils