Modélisation bayésienne et robotique

This document describes my research around Bayesian modeling and robotics. My work started with the modeling of biological processes before evolving towards robotics. In both cases, I was interested in both perception and action. I first proposed a model of human perception of planar surfaces with optic flow which fuses in a single framework two concurrent hypotheses of the literature. I also proposed and compared several models of eye movement selection in a Multiple Object Tracking task. I was able to show that the model with explicit uncertainty was the closest to the subjects eye movements.In robotics, I worked on the state estimation of several robots with classical filtering techniques but also including fusion of multiple sources of information of various nature and characteristics. I also discuss the Iterative Closest Point algorithm for which we proposed a more rigorous method for evaluating the different variants. The last piece of work I present deals with online three-dimensional path planning and execution of a tracked robot with significant climbing capabilities.I conclude this document with perspectives on what I call situated robotics, that is robots not taken in isolation but embedded in a sensorized environment shared with humans.Ce document décrit mes travaux de recherche autour de la modélisation bayésienne et de la robotique. Mon travail a commencé par la modélisation de processus biologiques avant, dans un deuxième temps, d'évoluer vers la robotique. Dans les deux cas, je me suis intéressé à la fois à la perception et à l'action. J'ai donc proposé un modèle de la perception humaine de plans par le flux optique qui réunit deux hypothèses de la littérature dans un cadre unique. J'ai aussi proposé et comparé différents modèle de la sélection de mouvement oculaire dans une tâche de suivi multi-cibles, et montré que le modèle prenant en compte explicitement l'incertitude proposait des mouvements plus proches de ceux des sujets.Du côté robotique, j'ai travaillé sur l'estimation d'état de plusieurs robots avec des techniques classiques de filtrage mais en incluant la fusion de plusieurs sources d'informations de nature et caractéristiques différentes. Je discute aussi de l'algorithme d'Iterative Closest Point pour proposer une méthode plus rigoureuse d'évaluation des différentes variantes. Le dernier travail que je présente concerne la planification en ligne et l'exécution de chemin pour un robot à chenille avec des capacités de franchissement importantes.Je conclus ce document par des perspectives de travail sur ce que j'appelle la robotique située, c'est-à-dire des robots non plus isolés mais plongés dans un environnement équipé de capteurs et partagé avec des humains

Transgenerational Effects of Parental Larval Diet on Offspring Development Time, Adult Body Size and Pathogen Resistance in Drosophila melanogaster

Environmental conditions experienced by parents are increasingly recognized to affect offspring performance. We set out to investigate the effect of parental larval diet on offspring development time, adult body size and adult resistance to the bacterium Serratia marcescens in Drosophila melanogaster. Flies for the parental generation were raised on either poor or standard diet and then mated in the four possible sex-by-parental diet crosses. Females that were raised on poor food produced larger offspring than females that were raised on standard food. Furthermore, male progeny sired by fathers that were raised on poor food were larger than male progeny sired by males raised on standard food. Development times were shortest for offspring whose one parent (mother or the father) was raised on standard and the other parent on poor food and longest for offspring whose parents both were raised on poor food. No evidence for transgenerational effects of parental diet on offspring disease resistance was found. Although paternal effects have been previously demonstrated in D. melanogaster, no earlier studies have investigated male-mediated transgenerational effects of diet in this species. The results highlight the importance of not only considering the relative contribution each parental sex has on progeny performance but also the combined effects that the two sexes may have on offspring performance

Data from: Intra- and trans-generational effects of larval diet on susceptibility to an entomopathogenic fungus, Beauveria bassiana, in the greater wax moth, Galleria mellonella

In addition to nutritional conditions experienced by individuals themselves, those experienced by their parents can affect their immune function. Here, we studied the intra- and trans-generational effects of larval diet on susceptibility to an entomopathogenic fungus, Beauveria bassiana, in the greater wax moth, Galleria mellonella. In the first part of the study, a split-brood design was used to compare the susceptibility of full sibs raised either on low- or on high-nutrition larval diet. In the second part of the study, a similar experimental design was employed to investigate the effects of maternal and paternal diet as well as their interaction on offspring's susceptibility. In the first part of the study, we found that individuals fed with high-nutrition diet had higher mortality from infection than individuals fed with low-nutrition diet. However, diet did not affect post-infection survival time. Conversely, in the second part of the study, maternal diet was found to have no significant effect on final mortality rate of offspring, but it affected survival time: larvae with high-nutrition maternal diet survived fewer days after infection than larvae with low-nutrition maternal diet. Paternal diet had no significant effect on offspring's susceptibility to the fungus, indicating that paternal effects are not as important as maternal effects in influencing immune function in this species. Our findings provide further indication that maternal nutrition affects immune function in insects, and suggest that the direct effects of nutrition on immunity may be different, yet parallel, to those caused by parental nutrition

Summary of analysis of variance on offspring body size (thorax length) with data pooled over sexes. Significant effects are shown in bold.

<p>*Error term used for the test of significance: SS = 1.007, df = 246.321.</p><p>**Error term used for the test of significance: SS = 1.007, df = 246.470.</p>†<p>Error term used for the test of significance: SS = 0.423, df = 262.208.</p>††<p>Error term used for the test of significance: SS = 1.007, df = 246.559.</p>‡<p>Error term used for the test of significance: SS = 0.425, df = 263.097.</p>‡‡<p>Error term used for the test of significance: SS = 0.427, df = 264.103.</p>+<p>Error term used for the test of significance: SS = 0.428, df = 264.718.</p>++<p>Error term used for the test of significance: SS = 0.337, df = 221.</p

Binary logistic regression analysis was used to identify factors associated with pathogen resistance.

<p>Survival among the disease treated flies was worse than among the control flies.</p><p>Overall percentage of cases correctly classified by the model: 86.1%.</p><p>Omnibus Tests of Model coefficients: P<0.001.</p><p>Hosmer-Lemeshow Goodness of Fit Test: P = 0.039.</p><p>Nagelkerke R Square: 0.418.</p

Binary logistic regression analysis was used to identify factors associated with pathogen resistance (disease treatment).

<p>Overall percentage of cases correctly classified by the model: 77.5%.</p><p>Omnibus Tests of Model coefficients: P<0.001.</p><p>Hosmer-Lemeshow Goodness of Fit Test: P = 0.675.</p><p>Nagelkerke R Square: 0.322.</p

Kaplan-Meier survival analysis was used for the comparisons of development times.

<p>A reduced probability value of P = 0.05/6 = 0.008 was used to control for multiple comparisons. All comparisons were statistically significant except for that between the progeny of S-P and P-S parents.</p><p>Log Rank (Mantel-Cox) statistics are reported.</p