Behavioral repertoire learning in robotics

Behavioral Repertoire Learning in Robotics Antoine Cully ISIR, Université Pierre et Marie Curie-Paris 6, CNRS UMR 7222 4 place Jussieu, F-75252, Paris Cedex 05, France [email protected] Jean-Baptiste Mouret ISIR, Université Pierre et Marie Curie-Paris 6, CNRS UMR 7222 4 place Jussieu, F-75252, Paris Cedex 05, France [email protected] ABSTRACT Learning in robotics typically involves choosing a simple goal (e.g. walking) and assessing the performance of each con- troller with regard to this task (e.g. walking speed). How- ever, learning advanced, input-driven controllers (e.g. walk- ing in each direction) requires testing each controller on a large sample of the possible input signals. This costly pro- cess makes difficult to learn useful low-level controllers in robotics. Here we introduce BR-Evolution, a new evolutionary learn- ing technique that generates a behavioral repertoire by tak- ing advantage of the candidate solutions that are usually discarded. Instead of evolving a single, general controller, BR-evolution thus evolves a collection of simple controllers, one for each variant of the target behavior; to distinguish similar controllers, it uses a performance objective that al- lows it to produce a collection of diverse but high-performing behaviors. We evaluated this new technique by evolving gait controllers for a simulated hexapod robot. Results show that a single run of the EA quickly finds a collection of controllers that allows the robot to reach each point of the reachable space. Overall, BR-Evolution opens a new kind of learning algorithm that simultaneously optimizes all the achievable behaviors of a robot

Novelty Search in Competitive Coevolution

One of the main motivations for the use of competitive coevolution systems is their ability to capitalise on arms races between competing species to evolve increasingly sophisticated solutions. Such arms races can, however, be hard to sustain, and it has been shown that the competing species often converge prematurely to certain classes of behaviours. In this paper, we investigate if and how novelty search, an evolutionary technique driven by behavioural novelty, can overcome convergence in coevolution. We propose three methods for applying novelty search to coevolutionary systems with two species: (i) score both populations according to behavioural novelty; (ii) score one population according to novelty, and the other according to fitness; and (iii) score both populations with a combination of novelty and fitness. We evaluate the methods in a predator-prey pursuit task. Our results show that novelty-based approaches can evolve a significantly more diverse set of solutions, when compared to traditional fitness-based coevolution.Comment: To appear in 13th International Conference on Parallel Problem Solving from Nature (PPSN 2014

Professional training and participatory research: Combined actions for developing organic rice farming in the Camargue region of France

In 2006 and 2007, INRA’s Joint Research Unit, Innovation, was a partner in a European professional training project within the framework of the Leonardo da Vinci programme. The objective of this project was to help develop organic rice farming in the major European rice-growing regions where rice is mainly cultivated in ecologically-sensitive areas. In France, the rate of conversion to organic production is much lower that what would be expected, since organic rice farming presents particular technical problems. The availability of expert support is critical to successful conversion and no structured training was available in the past. This is the reason why we developed a participatory training method that helps rice growers and stakeholders to convert to organic farming and to improve their organic rice production. Different training sessions were organised. The participants shared their thoughts about technical problems encountered and identified possible solutions. Some of the topics developed were weeds, soils and fertility, and varieties. At the end of these sessions, a motivated workgroup was set up. Some of its members even proposed to assess the efficiency of some of the techniques that were discussed during the work sessions in fields on their own farms. Furthermore, field visits were organised in the Camargue region of France and in Spain. Scientists and group members hope to be able to continue to work together after the O.R.P.E.S.A. project is over. In order to make this possible, we are now planning to initiate new research and development actions using the same approach

Quality-diversity optimization: a novel branch of stochastic optimization

Traditional optimization algorithms search for a single global optimum that maximizes (or minimizes) the objective function. Multimodal optimization algorithms search for the highest peaks in the search space that can be more than one. Quality-Diversity algorithms are a recent addition to the evolutionary computation toolbox that do not only search for a single set of local optima, but instead try to illuminate the search space. In effect, they provide a holistic view of how high-performing solutions are distributed throughout a search space. The main differences with multimodal optimization algorithms are that (1) Quality-Diversity typically works in the behavioral space (or feature space), and not in the genotypic (or parameter) space, and (2) Quality-Diversity attempts to fill the whole behavior space, even if the niche is not a peak in the fitness landscape. In this chapter, we provide a gentle introduction to Quality-Diversity optimization, discuss the main representative algorithms, and the main current topics under consideration in the community. Throughout the chapter, we also discuss several successful applications of Quality-Diversity algorithms, including deep learning, robotics, and reinforcement learning

Quality-diversity optimization: a novel branch of stochastic optimization

Traditional optimization algorithms search for a single global optimum that maximizes (or minimizes) the objective function. Multimodal optimization algorithms search for the highest peaks in the search space that can be more than one. Quality-Diversity algorithms are a recent addition to the evolutionary computation toolbox that do not only search for a single set of local optima, but instead try to illuminate the search space. In effect, they provide a holistic view of how high-performing solutions are distributed throughout a search space. The main differences with multimodal optimization algorithms are that (1) Quality-Diversity typically works in the behavioral space (or feature space), and not in the genotypic (or parameter) space, and (2) Quality-Diversity attempts to fill the whole behavior space, even if the niche is not a peak in the fitness landscape. In this chapter, we provide a gentle introduction to Quality-Diversity optimization, discuss the main representative algorithms, and the main current topics under consideration in the community. Throughout the chapter, we also discuss several successful applications of Quality-Diversity algorithms, including deep learning, robotics, and reinforcement learning

French lag in scientific research on organic farming: a scientometric approach

France suffers from a large research deficit in most areas related to sustainable development and public health. The case of research on organic farming, within the framework of the broader sector of agronomic research, constitutes a largely under-investigated field. Even if the demand for organic products is rapidly growing in Europe, research in this field still suffers from a lack of funding and researchers. A bibliometric analysis based on key words from scientific publications (in English only) taken from Thomson Scientific’s ISI Web of Science reference database made it possible to determine sufficiently relevant indicators for a comparison of national research efforts from 2000-2006, and to therefore assess actual research priorities in the area of organic farming. Beyond traditional specialisation analyses, the ratio between the world share of publications of a given country in organic farming and its world share of publications in specific disciplines and sub-disciplines (according to the nomenclature of the French Observatory for Sciences and Techniques) allowed us to obtain these prioritisation indexes that act as policy and priority (or prioritisation activity) indicators for research institutions involved in the concerned area. An index above 1 indicates an over-specialisation, whereas an index below 1 indicates an under-specialisation. For the period 2000-2006, the European Union obtained a specialisation index of 1.52 in the area of organic farming, compared to 0.68 for the US, 0.98 for Brazil and 0.18 for China. However, this seemingly satisfactory average for Europe as a whole hides important disparities between European countries. Moreover, France is at the very end of the classification for all indexes with only 0.47 specialisation, compared to Germany with 1.19, Italy with 1.39, Austria with 2.78, Sweden with 3.99, Finland with 4.46 and Denmark with 12.19. The prioritisation index for organic farming in comparison to the discipline, “Applied biology and ecology”, is 1.65 for the EU27, 0.69 for the US, 1.7 for Germany, 3.98 for Austria, 3.84 for Finland, 9.45 for Denmark and 0.49 for France. In comparison to the subdiscipline, “Agriculture, plant biology” the index is 1.5 for the EU27, 0.98 for the US, 1.61 for Germany, 3.28 for Austria, 1.52 for Finland, 9.79 for Denmark, and 0.41 for France. Finally, for the sub-discipline, “Agro-food”, it is 1.86 for the EU27, 0.73 for the US, 1.98 for Germany, 6.92 for Austria, 7.39 for Finland, 9.39 for Denmark and 0.58 for France. These results confirm that research on organic farming is largely under-prioritised in France. The French national research effort is therefore far from meeting the ecological and economic challenges facing agriculture in the 21st century

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