Scaling MAP-Elites to Deep Neuroevolution

Quality-Diversity (QD) algorithms, and MAP-Elites (ME) in particular, have proven very useful for a broad range of applications including enabling real robots to recover quickly from joint damage, solving strongly deceptive maze tasks or evolving robot morphologies to discover new gaits. However, present implementations of MAP-Elites and other QD algorithms seem to be limited to low-dimensional controllers with far fewer parameters than modern deep neural network models. In this paper, we propose to leverage the efficiency of Evolution Strategies (ES) to scale MAP-Elites to high-dimensional controllers parameterized by large neural networks. We design and evaluate a new hybrid algorithm called MAP-Elites with Evolution Strategies (ME-ES) for post-damage recovery in a difficult high-dimensional control task where traditional ME fails. Additionally, we show that ME-ES performs efficient exploration, on par with state-of-the-art exploration algorithms in high-dimensional control tasks with strongly deceptive rewards.Comment: Accepted to GECCO 202

A single polyploidization event at the origin of the tetraploid genome of Coffea arabica is responsible for the extremely low genetic variation in wild and cultivated germplasm

The genome of the allotetraploid species Coffea arabica L. was sequenced to assemble independently the two component subgenomes (putatively deriving from C. canephora and C. eugenioides) and to perform a genome-wide analysis of the genetic diversity in cultivated coffee germplasm and in wild populations growing in the center of origin of the species. We assembled a total length of 1.536 Gbp, 444 Mb and 527 Mb of which were assigned to the canephora and eugenioides subgenomes, respectively, and predicted 46,562 gene models, 21,254 and 22,888 of which were assigned to the canephora and to the eugeniodes subgenome, respectively. Through a genome-wide SNP genotyping of 736 C. arabica accessions, we analyzed the genetic diversity in the species and its relationship with geographic distribution and historical records. We observed a weak population structure due to low-frequency derived alleles and highly negative values of Taijma's D, suggesting a recent and severe bottleneck, most likely resulting from a single event of polyploidization, not only for the cultivated germplasm but also for the entire species. This conclusion is strongly supported by forward simulations of mutation accumulation. However, PCA revealed a cline of genetic diversity reflecting a west-to-east geographical distribution from the center of origin in East Africa to the Arabian Peninsula. The extremely low levels of variation observed in the species, as a consequence of the polyploidization event, make the exploitation of diversity within the species for breeding purposes less interesting than in most crop species and stress the need for introgression of new variability from the diploid progenitors

Evaluating Mixed-Initiative Procedural Level Design Tools using a Triple-Blind Mixed-Method User Study

Results from a triple-blind mixed-method user study into the effectiveness of mixed-initiative tools for the procedural generation of game levels are presented. A tool which generates levels using interactive evolutionary optimisation was designed for this study which (a) is focused on supporting the designer to explore the design space and (b) only requires the designer to interact with it by designing levels. The tool identifies level design patterns in an initial hand-designed map and uses that information to drive an interactive optimisation algorithm. A rigorous user study was designed which compared the experiences of designers using the mixed-initiative tool to designers who were given a tool which provided completely random level suggestions. The designers using the mixed-initiative tool showed an increased engagement in the level design task, reporting that it was effective in inspiring new ideas and design directions. This provides significant evidence that procedural content generation can be used as a powerful tool to support the human design process

Decentralized Unknown Building Exploration by Frontier Incentivization and Voronoi Segmentation in a Communication Restricted Domain

Exploring unknown environments using multiple robots poses a complex challenge, particularly in situations where communication between robots is either impossible or limited. Existing exploration techniques exhibit research gaps due to unrealistic communication assumptions or the computational complexities associated with exploration strategies in unfamiliar domains. In our investigation of multi-robot exploration in unknown areas, we employed various exploration and coordination techniques, evaluating their performance in terms of robustness and efficiency across different levels of environmental complexity. Our research is centered on optimizing the exploration process through strategic agent distribution. We initially address the challenge of city roadway coverage, aiming to minimize the travel distance of each agent in a scenario involving multiple agents to enhance overall system efficiency. To achieve this, we partition the city into subregions. and utilize Voronoi relaxation to optimize the size of postman distances for these subregions. This technique highlights the essential elements of an efficient city exploration. Expanding our exploration techniques to unknown buildings, we develop strategies tailored to this specific domain. After a careful evaluation of various exploration techniques, we introduce another goal selection strategy, Unknown Closest. This strategy combines the advantages of a greedy approach with the improved dispersal of agents, achieved through the randomization effect of a larger goal set. We further assess the exploration techniques in environments with restricted communication, presenting upper coordination mechanisms such as frontier incentivization and area segmentation. These methods enhance exploration performance by promoting independence and implicit coordination among agents. Our simulations demonstrate the successful application of these techniques in various complexity of interiors. In summary, this dissertation offers solutions for multi-robot exploration in unknown domains, paving the way for more efficient, cost-effective, and adaptable exploration strategies. Our findings have significant implications for various fields, ranging from autonomous city-wide monitoring to the exploration of hazardous interiors, where time-efficient exploration is crucial

Learned Contextual LiDAR Informed Visual Search in Unseen Environments

This paper presents LIVES: LiDAR Informed Visual Search, an autonomous planner for unknown environments. We consider the pixel-wise environment perception problem where one is given 2D range data from LiDAR scans and must label points contextually as map or non-map in the surroundings for visual planning. LIVES classifies incoming 2D scans from the wide Field of View (FoV) LiDAR in unseen environments without prior map information. The map-generalizable classifier is trained from expert data collected using a simple cart platform equipped with a map-based classifier in real environments. A visual planner takes contextual data from scans and uses this information to plan viewpoints more likely to yield detection of the search target. While conventional frontier based methods for LiDAR and multi sensor exploration effectively map environments, they are not tailored to search for people indoors, which we investigate in this paper. LIVES is baselined against several existing exploration methods in simulation to verify its performance. Finally, it is validated in real-world experiments with a Spot robot in a 20x30m indoor apartment setting. Videos of experimental validation can be found on our project website at https://sites.google.com/view/lives-icra-2024/home.Comment: 6 pages + references. 6 figures. 1 algorithm. 1 tabl

Image-based tree variations

The automatic generation of realistic vegetation closely reproducing the appearance of specific plant species is still a challenging topic in computer graphics. In this paper, we present a new approach to generate new tree models from a small collection of frontal RGBA images of trees. The new models are represented either as single billboards (suitable for still image generation in areas such as architecture rendering) or as billboard clouds (providing parallax effects in interactive applications). Key ingredients of our method include the synthesis of new contours through convex combinations of exemplar countours, the automatic segmentation into crown/trunk classes and the transfer of RGBA colour from the exemplar images to the synthetic target. We also describe a fully automatic approach to convert a single tree image into a billboard cloud by extracting superpixels and distributing them inside a silhouette-defined 3D volume. Our algorithm allows for the automatic generation of an arbitrary number of tree variations from minimal input, and thus provides a fast solution to add vegetation variety in outdoor scenes.Peer ReviewedPostprint (author's final draft

Procedural modeling of cities with semantic information for crowd simulation

En aquesta tesi de màster es presenta un sistema per a la generació procedural de ciutats poblades. Avui en dia poblar entorns virtuals grans tendeix a ser una tasca que requereix molt d’esforç i temps, i típicament la feina d’artistes o programadors experts. Amb aquest sistema es vol proporcionar una eina que permeti als usuaris generar entorns poblats d’una manera més fàcil i ràpida, mitjançat l’ús de tècniques procedurals. Les contribucions principals inclouen: la generació d’una ciutat virtual augmentada semànticament utilitzant modelat procedural basat en gramàtiques de regles, la generació dels seus habitants virtuals utilitzant dades estadístiques reals, i la generació d’agendes per a cada individu utilitzant també un mètode procedural basat en regles, el qual combina la informació semàntica de la ciutat amb les característiques i necessitats dels agents autònoms. Aquestes agendes individuals són usades per a conduir la simulació dels habitants, i poden incloure regles com a tasques d’alt nivell, l’avaluació de les quals es realitza al moment de començar-les. Això permet simular accions que depenguin del context, i interaccions amb altres agents.En esta tesis de máster se presenta un sistema para la generación procedural de ciudades pobladas. Hoy en día poblar entornos virtuales grandes tiende a ser una tarea que requiere de mucho tiempo y esfuerzo, y típicamente el trabajo de artistas o programadores expertos. Con este sistema se pretende proporcionar una herramienta que permita a los usuarios generar entornos poblados de un modo más fácil y rápido, mediante el uso de técnicas procedurales. Las contribuciones principales incluyen: la generación de una ciudad virtual aumentada semánticamente utilizando modelado procedural basado en gramáticas de reglas, la generación de sus habitantes virtuales utilizando datos estadísticos reales, y la generación de agendas para cada individuo utilizando también un método procedural basado en reglas, el cual combina la información semántica de la ciudad con las características y necesidades de los agentes autónomos. Estas agendas individuales son usadas para conducir la simulación de los habitantes, y pueden incluir reglas como tareas de alto nivel, la evaluación de las cuales se realiza cuando empiezan. Esto permite simular acciones que dependan del contexto, e interacciones con otros agentes.In this master thesis a framework for procedural generation of populated cities is presented. Nowadays, the population of large virtual environments tends to be a time-consuming task, usually requiring the work of expert artists or programmers. With this system we aim at providing a tool that can allow users to generate populated environments in an easier and faster way, by relying on the usage of procedural techniques. Our main contributions include: a generation of semantically augmented virtual cities using procedural modelling based on rule grammars, a generation of a virtual population using real-world data, and a generation of agendas for each individual inhabitant by using a procedural rule-based approach, which combines the city semantics with the autonomous agents characteristics and needs. The individual agendas are then used to drive a crowd simulation in the environment, and may include high-level rule tasks whose evaluation is delayed until they get triggered. This feature allows us to simulate context-dependant actions and interactions with other agents