Aprendizaje robótico por imitación utilizando imágenes 2D y 3D

Resumen Cada vez es más común encontrar robots realizando tareas en áreas compartidas con humanos, donde se espera que sean capaces de aprender de las acciones realizadas por otros y de adaptarse a nuevas situaciones. La aproximación más utilizada es aprendizaje por imitación, donde el robot es capaz de aprender a partir de la observación de la tarea siendo realizada por un operario. Luego de comparar varias de las técnicas de programación por demostración, se seleccionan las primitivas de movimiento dinámico (DMP) con reconstrucción utilizando regresión de procesos gaussianos (GPR). Las DMP codifican cada uno de las trayectorias dadas por los grados de libertad pertinentes a la acción a aprender, en este caso, llevar la mano hacia un objeto ubicado sobre una mesa. Por otro lado, GPR permite generalizar los movimientos del entrenamiento a nuevas trayectorias, cuando cambian tanto la posición inicial de la mano como la ubicación del objeto. Se realizó una comparación de varias técnicas de aprendizaje, teniendo en cuenta el error al objetivo, la correlación cruzada entre las señales de entrada y salida, y el tiempo de codificación y reconstrucción de la trayectoria. Además, la técnica de generalización se compara contra un algoritmo basado en distancia de Mahalanobis y distribución gaussiana, que utiliza los datos de la trayectoria sin codificar para realizar la estimación. La técnica regresión de procesos gaussianos, presentó un mayor desempeño al probarlo con 30 puntos de consulta para el valor inicial de la mano, y 30 puntos para la posición final o posición del objeto. La técnica de regresión de procesos gaussianos junto a primitivas de movimiento dinámico, se presenta como una solución para el aprendizaje por imitación de tareas, así como para la generalización a nuevas trayectorias a partir de la base de datos, al presentar bajos tiempos de codificación y errores pequeños con respecto a los valores objetivo.Abstract: It is becoming increasingly common to find robots performing tasks in shared areas with humans, they are expected to be able to learn from the actions taken by others and adapt to new situations. The most widely used approach is learning by imitation, where the robot is able to learn from watching the task being performed by an operator. After comparing several programming by demonstration techniques, the dynamic movement primitives (DMP) with reconstruction using Gaussian process regression (GPR) was selected. DMP encodes each of the paths given by the relevant degrees of freedom to bring the hand toward an object placed on a table. Furthermore, GPR allows to generalize the training movements to new paths when changing both the initial hand position and the location of the object. A comparison of various learning techniques was performed, considering the error to the target, the cross-correlation between the input and output signals, and time of coding and reconstruction of the trajectory. Besides, the technique is compared against a generalization based on the Mahalanobis distance and Gaussian distribution, which uses data from uncoded trajectories for the estimate. The Gaussian process regression technique, presented a better performance when tested with 30 queue points for the initial value of the hand, and 30 points for the final position of the object. Gaussian process regression along dynamic movement primitives is presented as a solution for learning by imitation of task, as well as generalization to new paths from the database, because of its fast encoding times and small errors regarding the target values.Maestrí

Visual Attention in Dynamic Environments and its Application to Playing Online Games

Abstract In this thesis we present a prototype of Cognitive Programs (CPs) - an executive controller built on top of Selective Tuning (ST) model of attention. CPs enable top-down control of visual system and interaction between the low-level vision and higher-level task demands. Abstract We implement a subset of CPs for playing online video games in real time using only visual input. Two commercial closed-source games - Canabalt and Robot Unicorn Attack - are used for evaluation. Their simple gameplay and minimal controls put the emphasis on reaction speed and attention over planning. Abstract Our implementation of Cognitive Programs plays both games at human expert level, which experimentally proves the validity of the concept. Additionally we resolved multiple theoretical and engineering issues, e.g. extending the CPs to dynamic environments, finding suitable data structures for describing the task and information flow within the network and determining the correct timing for each process

Computational aspects of cellular intelligence and their role in artificial intelligence.

The work presented in this thesis is concerned with an exploration of the computational aspects of the primitive intelligence associated with single-celled organisms. The main aim is to explore this Cellular Intelligence and its role within Artificial Intelligence. The findings of an extensive literature search into the biological characteristics, properties and mechanisms associated with Cellular Intelligence, its underlying machinery - Cell Signalling Networks and the existing computational methods used to capture it are reported. The results of this search are then used to fashion the development of a versatile new connectionist representation, termed the Artificial Reaction Network (ARN). The ARN belongs to the branch of Artificial Life known as Artificial Chemistry and has properties in common with both Artificial Intelligence and Systems Biology techniques, including: Artificial Neural Networks, Artificial Biochemical Networks, Gene Regulatory Networks, Random Boolean Networks, Petri Nets, and S-Systems. The thesis outlines the following original work: The ARN is used to model the chemotaxis pathway of Escherichia coli and is shown to capture emergent characteristics associated with this organism and Cellular Intelligence more generally. The computational properties of the ARN and its applications in robotic control are explored by combining functional motifs found in biochemical network to create temporal changing waveforms which control the gaits of limbed robots. This system is then extended into a complete control system by combining pattern recognition with limb control in a single ARN. The results show that the ARN can offer increased flexibility over existing methods. Multiple distributed cell-like ARN based agents termed Cytobots are created. These are first used to simulate aggregating cells based on the slime mould Dictyostelium discoideum. The Cytobots are shown to capture emergent behaviour arising from multiple stigmergic interactions. Applications of Cytobots within swarm robotics are investigated by applying them to benchmark search problems and to the task of cleaning up a simulated oil spill. The results are compared to those of established optimization algorithms using similar cell inspired strategies, and to other robotic agent strategies. Consideration is given to the advantages and disadvantages of the technique and suggestions are made for future work in the area. The report concludes that the Artificial Reaction Network is a versatile and powerful technique which has application in both simulation of chemical systems, and in robotic control, where it can offer a higher degree of flexibility and computational efficiency than benchmark alternatives. Furthermore, it provides a tool which may possibly throw further light on the origins and limitations of the primitive intelligence associated with cells

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Second Conference on NDE for Aerospace Requirements

Nondestructive evaluation and inspection procedures must constantly improve rapidly in order to keep pace with corresponding advances being made in aerospace material and systems. In response to this need, the 1989 Conference was organized to provide a forum for discussion between the materials scientists, systems designers, and NDE engineers who produce current and future aerospace systems. It is anticipated that problems in current systems can be resolved more quickly and that new materials and structures can be designed and manufactured in such a way as to be more easily inspected and to perform reliably over the life cycle of the system

Research reports: 1991 NASA/ASEE Summer Faculty Fellowship Program

The basic objectives of the programs, which are in the 28th year of operation nationally, are: (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate an exchange of ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of the participants' institutions; and (4) to contribute to the research objectives of the NASA Centers. The faculty fellows spent 10 weeks at MSFC engaged in a research project compatible with their interests and background and worked in collaboration with a NASA/MSFC colleague. This is a compilation of their research reports for summer 1991