We propose a developmental framework that enables the robot to learn affordances through interaction with the environment in an unsupervised way and to use these affordances at different levels of robot control, ranging from reactive response to planning. Inspired from Developmental Psychology, the robot&rsquo / s discovery of action possibilities is realized in two sequential phases. In the first phase, the robot that initially possesses a limited number of basic actions and reflexes discovers new behavior primitives by exercising these actions and by monitoring the changes created in its initially crude perception system. In the second phase, the robot explores a more complicated environment by executing the discovered behavior primitives and using more advanced perception to learn further action possibilities. For this purpose, first, the robot discovers commonalities in action-effect experiences by finding effect categories, and then builds predictors for each behavior to map object features and behavior parameters into effect categories. After learning affordances through self-interaction and self-observation, the robot can make plans to achieve desired goals, emulate end states of demonstrated actions, monitor the plan execution and take corrective actions using the perceptual structures employed or discovered during learning. Mobile and manipulator robots were used to realize the proposed framework. Similar to infants, these robots were able to form behavior repertoires, learn affordances, and gain prediction capabilities. The learned affordances were shown to be relative to the robots, provide perceptual economy and encode general relations. Additionally, the affordance-based planning ability was verified in various tasks such as table cleaning and object transportation.

Experience forms the basis of learning. It is crucial in the development of human intelligence, and more broadly allows an agent to discover and learn about the world around it. Although experience is fundamental to learning, it is costly and time-consuming to obtain. In order to speed this process up, humans in particular have developed communication abilities so that ideas and knowledge can be shared without requiring first-hand experience. Consider the same need for knowledge sharing among robots. Based on the recent growth of the field, it is reasonable to assume that in the near future there will be a collection of robots learning to perform tasks and gaining their own experiences in the world. In order to speed this learning up, it would be beneficial for the various robots to share their knowledge with each other. In most cases, however, the communication of knowledge among humans relies on the existence of similar sensory and motor capabilities. Robots, on the other hand, widely vary in perceptual and motor apparatus, ranging from simple light sensors to sophisticated laser and vision sensing. This dissertation defines the problem of how heterogeneous robots with widely different capabilities can share experiences gained in the world in order to speed up learning. The work focus specifically on differences in sensing and perception, which can be used both for perceptual categorization tasks as well as determining actions based on environmental features. Motivating the problem, experiments first demonstrate that heterogeneity does indeed pose a problem during the transfer of object models from one robot to another. This is true even when using state of the art object recognition algorithms that use SIFT features, designed to be unique and reproducible. It is then shown that the abstraction of raw sensory data into intermediate categories for multiple object features (such as color, texture, shape, etc.), represented as Gaussian Mixture Models, can alleviate some of these issues and facilitate effective knowledge transfer. Object representation, heterogeneity, and knowledge transfer is framed within Gärdenfors' conceptual spaces, or geometric spaces that utilize similarity measures as the basis of categorization. This representation is used to model object properties (e.g. color or texture) and concepts (object categories and specific objects). A framework is then proposed to allow heterogeneous robots to build models of their differences with respect to the intermediate representation using joint interaction in the environment. Confusion matrices are used to map property pairs between two heterogeneous robots, and an information-theoretic metric is proposed to model information loss when going from one robot's representation to another. We demonstrate that these metrics allow for cognizant failure, where the robots can ascertain if concepts can or cannot be shared, given their respective capabilities. After this period of joint interaction, the learned models are used to facilitate communication and knowledge transfer in a manner that is sensitive to the robots' differences. It is shown that heterogeneous robots are able to learn accurate models of their similarities and difference, and to use these models to transfer learned concepts from one robot to another in order to bootstrap the learning of the receiving robot. In addition, several types of communication tasks are used in the experiments. For example, how can a robot communicate a distinguishing property of an object to help another robot differentiate it from its surroundings? Throughout the dissertation, the claims will be validated through both simulation and real-robot experiments.

Many mobile robot applications require robots to act safely and intelligently in complex unfamiliarenvironments with little structure and limited or unavailable human supervision. As arobot is forced to operate in an environment that it was not engineered or trained for, various aspectsof its performance will inevitably degrade. Roboticists equip robots with powerful sensorsand data sources to deal with uncertainty, only to discover that the robots are able to make onlyminimal use of this data and still find themselves in trouble. Similarly, roboticists develop andtrain their robots in representative areas, only to discover that they encounter new situations thatare not in their experience base. Small problems resulting in mildly sub-optimal performance areoften tolerable, but major failures resulting in vehicle loss or compromised human safety are not.This thesis presents a series of online algorithms to enable a mobile robot to better deal withuncertainty in unfamiliar domains in order to improve its navigational abilities, better utilizeavailable data and resources and reduce risk to the vehicle. We validate these algorithms throughextensive testing onboard large mobile robot systems and argue how such approaches can increasethe reliability and robustness of mobile robots, bringing them closer to the capabilitiesrequired for many real-world applications.

Submitted by PPG Ci?ncia da Computa??o (ppgcc@pucrs.br) on 2018-06-07T11:44:28Z No. of bitstreams: 1 RENAN_GUEDES_MAIDANA_DIS.pdf: 4462325 bytes, checksum: 589fff5df748f66fa3f6b644cbc058db (MD5) / Approved for entry into archive by Sheila Dias (sheila.dias@pucrs.br) on 2018-06-15T14:20:14Z (GMT) No. of bitstreams: 1 RENAN_GUEDES_MAIDANA_DIS.pdf: 4462325 bytes, checksum: 589fff5df748f66fa3f6b644cbc058db (MD5) / Made available in DSpace on 2018-06-15T14:58:29Z (GMT). No. of bitstreams: 1 RENAN_GUEDES_MAIDANA_DIS.pdf: 4462325 bytes, checksum: 589fff5df748f66fa3f6b644cbc058db (MD5) Previous issue date: 2018-03-02 / Na ?rea da Rob?tica M?vel, o problema da localiza??o ? definido como a determina??o da posi??o e orienta??o de um rob? em um espa?o tri-dimensional atrav?s de informa??es de seus sensores. A solu??o mais comum para esse problema ? utilizar um receptor de GPS (doingl?s, Global Positioning System), que reporta posi??o absoluta com rela??o a um sistema de coordenadas fixo e centralizado na Terra. Por?m, o sinal de GPS ? muito afetado por condi??es ambientais e oclus?o de linha de vis?o, por vezes fornecendo estimativas de posi??o de baixa qualidade, se houverem .Com inspira??o nestes problemas, este projeto prop?e um sistema de localiza??o para ser usado por um rob? terrestre em um ambiente externo n?o-controlado, onde h? indisponibilidade de GPS ou que suas medidas s?o de baixa qualidade. Tendo em vista que sensores de baixo custo apresentam medi??es imprecisas devido a fatores ambientais (e.g. terreno acidentado), ? proposta a utiliza??o de pares receptor-transmissor de R?dio-Frequ?ncia, onde a medida do Indicador de Pot?ncia de Sinal Recebido ? usada para estimar as dist?ncias entre receptor e trans- missor, que s?o por sua vez usadas para posicionamento. Essa medida possuia vantagem de ser independente da ilumina??o do ambiente e do estado do terreno, que afetam outros m?todos de localiza??o como Odometria Visual ou por rodas. Um erro m?dio de posiciona- mento de 0.41m foi alcan?ado atrav?s da fus?o de odometria por rodas, velocidade angular de um girosc?pio e pot?ncia de sinal recebido, em um algoritmo de Filtro de Kalman Esten- dido Aumentado, comum a melhoria de 82.66% referente ao erro m?dio de 2.38 m obtido com um sensor GPS comum. / In the field of Mobile Robotics, the localization problem consists on determining a robot?s position and orientation in a three-dimensional space through sensor information. The most common solution to this problem is to employ a Global Positioning System receiver, also known as GPS, which reports absolute position in relation to an Earth-centered fixed coordinate system. However, GPS signals are greatly affected by atmospheric conditions and line-of-sight occlusion, sometimes providing very poor position estimates, if any at all. Inspired by these problems, this project proposes a localization system to be used by a robot in an uncontrolled outdoor environment, where GPS measurements are poor or unavailable. As common sensors provide inaccurate position estimates due to environmental factors (e.g. rough terrain), we propose the use of Radio-Frequency receiver-transmitter pairs, in which the Received Signal Strength Indicator is used for estimating the distances between receiver and transmitter, which in turn are used for positioning. This measurement has the advantage of being independent from lighting conditions or the state of the terrain, factors which affect other localization methods such as visual or wheel odometry. A mean positioning error of 0.41 m was achieved by fusing wheel odometry, angular velocity from a gyroscope and the received signal strength, in an Augmented Extended Kalman Filter algorithm, with an improvement of 82.66% relative to the mean error of 2.38 m obtained with a common GPS sensor.