Evolution provides a creative fount of complex and subtle adaptations that often surprise the scientists who discover them. However, the creativity of evolution is not limited to the natural world: Artificial organisms evolving in computational environments have also elicited surprise and wonder from the researchers studying them. The process of evolution is an algorithmic process that transcends the substrate in which it occurs. Indeed, many researchers in the field of digital evolution can provide examples of how their evolving algorithms and organisms have creatively subverted their expectations or intentions, exposed unrecognized bugs in their code, produced unexpectedly adaptations, or engaged in behaviors and outcomes, uncannily convergent with ones found in nature. Such stories routinely reveal surprise and creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. Bugs are fixed, experiments are refocused, and one-off surprises are collapsed into a single data point. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This article is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.

Our daily environments are complex, composed of objects with different features. These features can be categorized into low-level features, e.g., an object position or temperature, and high-level features resulting from a pre-processing of low-level features for decision purposes, e.g., a binary value saying if it is too hot to be grasped. Besides, our environments are dynamic, i.e., object states can change at any moment. Therefore, robots performing tasks in these environments must have the capacity to (i) identify the next action to execute based on the available low-level and high-level object states, and (ii) dynamically adapt their actions to state changes. We introduce a method named Interaction State-based Skill Learning (IS2L), which builds skills to solve tasks in realistic environments. A skill is a Bayesian Network that infers actions composed of a sequence of movements of the robot's end-effector, which locally adapt to spatio-temporal perturbations using a dynamical system. In the current paper, an external agent performs one or more kinesthetic demonstrations of an action generating a dataset of high-level and low-level states of the robot and the environment objects. First, the method transforms each interaction to represent (i) the relationship between the robot and the object and (ii) the next robot end-effector movement to perform at consecutive instants of time. Then, the skill is built, i.e., the Bayesian network is learned. While generating an action this skill relies on the robot and object states to infer the next movement to execute. This movement selection gets inspired by a type of predictive models for action selection usually called affordances. The main contribution of this paper is combining the main features of dynamical systems and affordances in a unique method to build skills that solve tasks in realistic scenarios. More precisely, combining the low-level movement generation of the dynamical systems, to adapt to local perturbations, with the next movement selection simultaneously based on high-level and low-level states. This contribution was assessed in three experiments in realistic environments using both high-level and low-level states. The built skills solved the respective tasks relying on both types of states, and adapting to external perturbations.

A robot designer can provide a robot with knowledge to perform tasks on an environment. However, this approach can limit the achievement of future tasks executed by the robot. Providing it with the ability to develop its own skills paves the way for robots that are not limited by design. In this work a task consists in reproducing a given set of effects on an object. A robot must accomplish this task with limited information about the object, learning affordances to reproduce the effects, increasing this information throughout consecutive interactions with the object. We propose a method named Adaptive Affordance Learning (A(2)L) which endows a robot with the capacity to learn affordances associated to an object, both adapting the robot's actions to the object position; and increases the robot's information about the object when needed. This paper presents two main contributions: first, an online adaption of the robot actions to interact with the object, decomposing each action into a sequence of movements, adapting each movement, in a close loop, to the object position; and second, to increase the information about the object, we propose an iterative process that alternates between (1) exploration of the environment interacting with the object, (2) affordance acquisition and (3) affordance validation. These contributions are assessed in two experiments where a simulated Baxter robot learns to push a box to different positions on a table.

A robot designer can provide a robot with knowledge to perform tasks on an environment. However, this approach can limit the achievement of future tasks executed by the robot. Providing it with the ability to develop its own skills paves the way for robots that are not limited by design. In this work a task consists in reproducing a given set of effects on an object. A robot must accomplish this task with limited information about the object, learning affordances to reproduce the effects, increasing this information throughout consecutive interactions with the object. We propose a method named Adaptive Affordance Learning (A2L) which endows a robot with the capacity to learn affordances associated to an object, both adapting the robot's actions to the object position; and increases the robot's information about the object when needed. This paper presents two main contributions: first, an online adaption of the robot actions to interact with the object, decomposing each action into a sequence of movements, adapting each movement, in a close loop, to the object position; and second, to increase the information about the object, we propose an iterative process that alternates between (1) exploration of the environment interacting with the object, (2) affordance acquisition and (3) affordance validation. These contributions are assessed in two experiments where a simulated Baxter robot learns to push a box to different positions on a table.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Bootstraping manipulation skills to learn affordances in open-ended environments Carlos Maestre, Christophe Gonzales, Stéphane Doncieux

Determining in advance all objects that a robot will interact with in an open environment is very challenging, if not impossible. It makes difficult the development of models that will allow to perceive and recognize objects, to interact with them and to predict how these objects will react to interactions with other objects or with the robot. Developmental robotics proposes to make robots learn by themselves such models through a dedicated exploration step. It raises a chicken-and-egg problem: the robot needs to learn about objects to discover how to interact with them and, to this end, it needs to interact with them. In this work, we propose Novelty-driven Evolutionary Babbling (NovEB), an approach enabling to bootstrap this process and to acquire knowledge about objects in the surrounding environment without requiring to include a priori knowledge about the environment, including objects, or about the means to interact with them. Our approach consists in using an evolutionary algorithm driven by a novelty criterion defined in the raw sensorimotor flow: behaviours, described by a trajectory of the robot end effector, are generated with the goal to maximize the novelty of raw perceptions. The approach is tested on a simulated PR2 robot and is compared to a random motor babbling.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Learning a high diversity of object manipulations though an evolutionary-based babbling Pierre Ecarlat, Antoine Cully, Carlos Maestre, Stéphane Doncieux

Our everyday environment contains many different objects and we are frequently confronted to new objects, may it be known objects with a new shape or color, or completely new objects (smartphones or tablet computers, for instance, did not exist at all in our environment a few years ago). A robot working in our environment should then be able to deal with such modifications. It should in particular be able to identify these objects and what to do with them, i.e. their affordances. Human infants learn these affordances through an interaction with the environment called body babbling [1]. Developmental robotics [2] encourages applying the same exploration step in robots. But, how to define an environment exploration strategy that would work on any kind of environment and object that the robot may encounter before knowing them and their features?

We provide the implementation of an innovative cloud based Farm Management System.It provides a framework that allows the interconnection among services developed by different service providers.It is open and flexible so it can be tailored to the needs of different farmers.We have utilized domain independent software modules called generic enablers, developed within the FI-WARE project. Recent technological advances have paved the way for developing and offering advanced services for the stakeholders in the agricultural sector. A paradigm shift is underway from proprietary and monolithic tools to Internet-based, cloud hosted, open systems that will enable more effective collaboration between stakeholders. This new paradigm includes the technological support of application developers to create specialized services that will seamlessly interoperate, thus creating a sophisticated and customisable working environment for the end users. We present the implementation of an open architecture that instantiates such an approach, based on a set of domain independent software tools called \"generic enablers\" that have been developed in the context of the FI-WARE project. The implementation is used to validate a number of innovative concepts for the agricultural sector such as the notion of a services' market place and the system's adaptation to network failures. During the design and implementation phase, the system has been evaluated by end users, offering us valuable feedback. The results of the evaluation process validate the acceptance of such a system and the need of farmers to have access to sophisticated services at affordable prices. A summary of this evaluation process is also presented in this paper.

Recent technological advances have paved the way for developing and offering advanced services for the stakeholders in the agricultural sector. A paradigm shift is underway from proprietary and monolithic tools to Internet-based, cloud hosted, open systems that will enable more effective collaboration between stakeholders. This new paradigm includes the technological support of application developers to create specialized services that will seamlessly interoperate, thus creating a sophisticated and customisable working environment for the end users. We present the implementation of an open architecture that instantiates such an approach, based on a set of domain independent software tools called “generic enablers” that have been developed in the context of the FI-WARE project. The implementation is used to validate a number of innovative concepts for the agricultural sector such as the notion of a services’ market place and the system’s adaptation to network failures. During the design and implementation phase, the system has been evaluated by end users, offering us valuable feedback. The results of the evaluation process validate the acceptance of such a system and the need of farmers to have access to sophisticated services at affordable prices. A summary of this evaluation process is also presented in this paper.

The Future Internet is expected to greatly influence how the food and agriculture sector is currently operating. In this paper, we present the specific characteristics of the agri-food sector focusing on how information management in this area will take place under a highly heterogeneous group of actors and services, based on the EU SmartAgriFood project. We also discuss how a new dynamic marketplace will be realized based on the adoption of a number of specialized software modules, called “Generic Enablers” that are currently developed in the context of the EU FI-WARE project. Thus, the paper presents the overall vision for data integration along the supply chain as well as the development and federation of Future Internet services that are expected to revolutionize the agriculture sector.

The Future Internet is expected to greatly influence how the food and agriculture sector is currently operating. In this paper, we present the specific characteristics of the agri-food sector focusing on how information management in this area will take place under a highly heterogeneous group of actors and services, based on the EU SmartAgriFood project. We also discuss how a new dynamic marketplace will be realized based on the adoption of a number of specialized software modules, called "Generic Enablers" that are currently developed in the context of the EU FI-WARE project. Thus, the paper presents the overall vision for data integration along the supply chain as well as the development and federation of Future Internet services that are expected to revolutionize the agriculture sector. © 2013 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of HAICTA.

This paper presents AFICUS, an architecture and implementation of a technology dedicated to pushing current Internet content consumer to become a prosumer (fully acting as content producer and consumer). To make this possible, AFICUS behaves as a middleware intercepting and analyzing the whole spectrum of the actual technological communications (mobile phone, social networks, IPTV, etc.). Thanks to this middle position, AFICUS is able to automatically enrich, augment, summarize, store, combine, adapt and distribute user-generated content without requiring any additional actions from the user. This kind of architecture opens new mechanisms for conceiving services and business models in the context of the Future Internet.

Génération autonome et en ligne de compétences déduisant des actions adaptées aux états contextuels de bas niveau et de haut niveau Les robots sont censés nous aider dans nos tâches quotidiennes. À cette fin, ils peuvent devoir effectuer différentes tâches dans des scénarios changeants. Le nombre de scénarios dissemblables auxquels un robot peut faire face est illimité. Par conséquent, il est plausible de penser qu’un robot doit apprendre de manière autonome pour effectuer des tâches. Une tâche consiste à générer un changement attendu, c’est-à-dire un effet, dans l’environnement, la configuration du robot, ou les deux. Par conséquent, le robot doit apprendre à effectuer la bonne action sur l’environnement pour obtenir l’effet attendu. Une approche de l’apprentissage de ces actions est à travers une interaction continue du robot avec son environnement en se concentrant sur ces actions produisant des effets sur l’environnement. La relation acquise de l’application d’une action sur un objet pour obtenir un effet est appelée affordance. Au cours des dernières années, de nombreux efforts de recherche ont été consacrés à l’apprentissage des affordances. Les travaux connexes couvrent l’apprentissage de simples actions saissir sur des scénarios de table à la définition d’architectures cognitives complexes. Ces travaux s’appuient sur différents blocs de construction, comme méthodes de vision pour identifier la position des objets ou des compétences sensorimotrices prédéfinies pour générer des effets sur un environnement contraint. L’utilisation d’actions prédéfinies facilite l’apprentissage des affordances, produisant une information riche et cohérente des changements produits sur un objet. Cependant, nous affirmons que l’utilisation de ces actions limite l’évolutivité des expériences disponibles aux environnements dynamiques et bruyants. Le travail actuel porte sur l’apprentissage autonome d’un ensemble de compétences sensorimotrices à travers des interactions avec un environnement. Chaque compétence doit générer une action continue pour reproduire un effet sur un objet, adapté à la position de l’objet. En outre, chaque compétence est simultanément adaptée aux perturbations de bas niveau, par ex. un changement dans la position de l’objet, et des changements contextuels de haut niveau, par ex. un poêle s’allume [...]