19 research outputs found
Expectations and expertise in artificial intelligence: specialist views and historical perspectives on conceptualisation, promise, and funding
Artificial intelligence’s (AI) distinctiveness as a technoscientific field that imitates the ability to think went through a resurgence of interest post-2010, attracting a flood of scientific and popular expectations as to its utopian or dystopian transformative consequences. This thesis offers observations about the formation and dynamics of expectations based on documentary material from the previous periods of perceived AI hype (1960-1975 and 1980-1990, including in-between periods of perceived dormancy), and 25 interviews with UK-based AI specialists, directly involved with its development, who commented on the issues during the crucial period of uncertainty (2017-2019) and intense negotiation through which AI gained momentum prior to its regulation and relatively stabilised new rounds of long-term investment (2020-2021). This examination applies and contributes to longitudinal studies in the sociology of expectations (SoE) and studies of experience and expertise (SEE) frameworks, proposing a historical sociology of expertise and expectations framework. The research questions, focusing on the interplay between hype mobilisation and governance, are: (1) What is the relationship between AI practical development and the broader expectational environment, in terms of funding and conceptualisation of AI? (2) To what extent does informal and non-developer assessment of expectations influence formal articulations of foresight? (3) What can historical examinations of AI’s conceptual and promissory settings tell about the current rebranding of AI?
The following contributions are made: (1) I extend SEE by paying greater attention to the interplay between technoscientific experts and wider collective arenas of discourse amongst non-specialists and showing how AI’s contemporary research cultures are overwhelmingly influenced by the hype environment but also contribute to it. This further highlights the interaction between competing rationales focusing on exploratory, curiosity-driven scientific research against exploitation-oriented strategies at formal and informal levels. (2) I suggest benefits of examining promissory environments in AI and related technoscientific fields longitudinally, treating contemporary expectations as historical products of sociotechnical trajectories through an authoritative historical reading of AI’s shifting conceptualisation and attached expectations as a response to availability of funding and broader national imaginaries. This comes with the benefit of better perceiving technological hype as migrating from social group to social group instead of fading through reductionist cycles of disillusionment; either by rebranding of technical operations, or by the investigation of a given field by non-technical practitioners. It also sensitises to critically examine broader social expectations as factors for shifts in perception about theoretical/basic science research transforming into applied technological fields. Finally, (3) I offer a model for understanding the significance of interplay between conceptualisations, promising, and motivations across groups within competing dynamics of collective and individual expectations and diverse sources of expertise
System Architectures for Cooperative Teams of Unmanned Aerial Vehicles Interacting Physically with the Environment
Unmanned Aerial Vehicles (UAVs) have become quite a useful tool for a wide range of
applications, from inspection & maintenance to search & rescue, among others. The
capabilities of a single UAV can be extended or complemented by the deployment
of more UAVs, so multi-UAV cooperative teams are becoming a trend. In that case,
as di erent autopilots, heterogeneous platforms, and application-dependent software
components have to be integrated, multi-UAV system architectures that are fexible
and can adapt to the team's needs are required.
In this thesis, we develop system architectures for cooperative teams of UAVs,
paying special attention to applications that require physical interaction with the
environment, which is typically unstructured. First, we implement some layers to
abstract the high-level components from the hardware speci cs. Then we propose
increasingly advanced architectures, from a single-UAV hierarchical navigation architecture
to an architecture for a cooperative team of heterogeneous UAVs. All
this work has been thoroughly tested in both simulation and eld experiments in
di erent challenging scenarios through research projects and robotics competitions.
Most of the applications required physical interaction with the environment, mainly
in unstructured outdoors scenarios. All the know-how and lessons learned throughout
the process are shared in this thesis, and all relevant code is publicly available.Los vehículos aéreos no tripulados (UAVs, del inglés Unmanned Aerial Vehicles) se han
convertido en herramientas muy valiosas para un amplio espectro de aplicaciones, como
inspección y mantenimiento, u operaciones de rescate, entre otras. Las capacidades de un
único UAV pueden verse extendidas o complementadas al utilizar varios de estos vehículos
simultáneamente, por lo que la tendencia actual es el uso de equipos cooperativos con
múltiples UAVs. Para ello, es fundamental la integración de diferentes autopilotos,
plataformas heterogéneas, y componentes software -que dependen de la aplicación-, por lo
que se requieren arquitecturas multi-UAV que sean flexibles y adaptables a las necesidades
del equipo.
En esta tesis, se desarrollan arquitecturas para equipos cooperativos de UAVs, prestando
una especial atención a aplicaciones que requieran de interacción física con el entorno,
cuya naturaleza es típicamente no estructurada. Primero se proponen capas para abstraer a
los componentes de alto nivel de las particularidades del hardware. Luego se desarrollan
arquitecturas cada vez más avanzadas, desde una arquitectura de navegación para un
único UAV, hasta una para un equipo cooperativo de UAVs heterogéneos. Todo el trabajo ha
sido minuciosamente probado, tanto en simulación como en experimentos reales, en
diferentes y complejos escenarios motivados por proyectos de investigación y
competiciones de robótica. En la mayoría de las aplicaciones se requería de interacción
física con el entorno, que es normalmente un escenario en exteriores no estructurado. A lo
largo de la tesis, se comparten todo el conocimiento adquirido y las lecciones aprendidas en
el proceso, y el código relevante está publicado como open-source
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Hypernetworks Analysis of RoboCup Interactions
Robotic soccer simulations are controlled environments in which the rich variety of interactions among agents make them good candidates to be studied as complex adaptive systems. The challenge is to create an autonomous team of soccer agents that can adapt and improve its behaviour as it plays other teams. By analogy with chess, the movements of the soccer agents and the ball form ever-changing networks as players in one team form structures that give their team an advantage. For example, the Defender’s Dilemma involves relationships between an attacker with the ball, a team-mate and a defender. The defender must choose between tackling the player with the ball, or taking a position to intercept a pass to the other attacker. Since these structures involve more that two interacting entities it is necessary to go beyond networks to multidimensional hypernetworks. In this context, this thesis investigates (i) is it possible to identify patterns of play, that lead a team to obtain an advantage ?, (ii) is it possible to forecast with a good degree of accuracy if a certain game action or sequence of game actions is going to be successful, before it has been completed ?, and (iii) is it possible to make behavioural patterns emerge in the game without specifying the behavioural rules in detail ? To investigate these research questions we devised two methods to analyse the interactions between robotic players, one based on traditional programming and one based on Deep Learning. The first method identified thousands of Defender’s Dilemma configurations from RoboCup 2D simulator games and found a statistically significant association between winning and the creation of the defender’s dilemma by the attackers of the winning team. The second method showed that a feedforward Artificial Neural Network trained on thousands of games can take as input the current game configuration and forecast to a high degree of accuracy if the current action will end up in a goal or not. Finally, we designed our own fast and simple robotic soccer simulator for investigating Reinforcement Learning. This showed that Reinforcement Learning using Proximal Policy Optimization could train two agents in the task of scoring a goal, using only basic actions without using pre-built hand-programmed skills. These experiments provide evidence that it is possible: to identify advantageous patterns of play; to forecast if an action or sequence of actions will be successful; and to make behavioural patterns emerge in the game without specifying the behavioural rules in detail
Mobile Manipulation Hackathon: Moving into Real World Applications
The Mobile Manipulation Hackathon was held in late 2018 during the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) to showcase the latest applications of wheeled robotic manipulators. The challenge had an open format, where teams developed an application using simulation tools and integrated it into a robotic platform. This article presents the competition and analyzes the results, with information gathered during the event and from a survey circulated among the finalist teams. We provide an overview of the mobile manipulation field, identify key areas required for further development to facilitate the implementation of mobile manipulators in real applications, and discuss ideas about how to structure future hackathon-style competitions to enhance their impact on the scientific and industrial communities.Peer ReviewedPostprint (published version
Control of heterogeneous robot networks for assistance in search and rescue tasks
This project develops a decentralized control strategy for multiple heterogeneous robots oriented to the assistance in search and rescue situations from two complementary perspectives, the discrete tasks allocation and the real-time control. For the discrete task allocation through the mission, we present an optimized algorithm based on events, oriented to the minimization of the time required to attend all the victims in the mission environment. This algorithm allows assign to each robot an appropriate task considering that the robots may vary in their capacity for completing each task and also may vary in their moving capabilities. The considered tasks are the mission environment exploration, the victims’ search and identification, the medical supplies delivery to victims unable to move and the evacuation of victims capable to move. It is worth to mention that, through the development of each task and the estimation of its durations, the robots consider optimized routes considering a distance metric based in the breath first search algorithm called flooding distance with 8 neighbors (8NF) which considers only orthogonal and 45 degrees diagonal movements allowing an estimation of the geodesic distance to each point in the map. Regarding to the real-time control laws, they oversee the proper execution of the tasks assigned by the reallocation algorithm respecting the restrictions in the connectivity range, the obstacles avoidance and the fulfillment of each task. The exploration task is made employing an adaptation of the algorithm DisCoverage presented by [16] which employing a Voronoi cells based tessellation considering the arrival time to each point as reference, allows the determination of the map of non-convex spaces as those that may be found in search and rescue situations. The evasion of obstacles and the preservation of the robots’ links is achieved employing an approach of artificial potentials based in the work of [37]. The interest points related to each task tracking is made employing proportional control loops for each agent identifying the route points within the line of sight and considering optimized routes given by the 8NF flooding distance metric. Additionally, there is presented a heuristic reconfiguration algorithm that allows to change the network topology preserving its connectivity for each instant of time. This complete framework allows a team of autonomous robots to bring valuable assistance in certain search and rescue situations where the human teams may be insufficient, and/or the mission conditions may be harmful for the people considering that even if the robots cannot realize paramedical tasks yet, they can complete multiple useful tasks for reducing the effort and risks of the human teams in that kind of situations. The functioning of those algorithms is presented in non-trivial simulations intended to show the behaviors that emerge in the robots.Resumen: Este proyecto desarrolló una estrategia de control descentralizado para múltiples robots heterogéneos orientada a la asistencia en situaciones de búsqueda y rescate desde dos perspectivas complementarias, la asignación discreta de tareas y el control en tiempo real. Para la asignación discreta de las tareas a los robots a lo largo de la misión, presentamos un algoritmo optimizado de reasignación de tareas basado en eventos, orientado a la minimización del tiempo requerido para atender a todas las víctimas en el ambiente de misión. Este algoritmo permite asignar a cada robot una tarea apropiada considerando que los robots pueden diferir en su capacidad para completar cada tarea, así como también en sus capacidades de movimiento. Las tareas consideradas son la exploración del ambiente de misión, la búsqueda e identificación de víctimas, la entrega de suministros médicos a las víctimas incapaces de moverse y la evacuación de las víctimas capaces de moverse. Cabe destacar que, durante el desarrollo de cada tarea y la estimación de los tiempos de las mismas, los robots consideran rutas optimizadas considerando una métrica de distancia basada en el algoritmo de búsqueda en anchura (Breath first Search) llamada distancia por inundaci´on con 8 vecinos (8NF) la cual considera movimientos netamente ortogonales y diagonales a 45 grados permitiendo una estimación de la distancia geodésica a cada punto en el mapa. Con respecto a las leyes de control en tiempo real, estas están a cargo de la correcta ejecución de las tareas asignadas por el algoritmo de reasignación de tareas respetando las restricciones en el rango de conectividad, la evasión de colisiones y la completa ejecución de cada tarea. La exploración es desarrollada empleando una adaptación del algoritmo DisCoverage presentado por [16] el cuál empleando una teselación basada en celdas de Voronoi con el tiempo de llegada a cada punto como referencia, permite la determinaci´on del mapa de espacios no convexos como los que se pueden encontrar en algunas situaciones de búsqueda y rescate. La evasión de obstáculos y la preservación de los enlaces se realiza a través de un enfoque de potenciales artificiales basándose en el trabajo de [37]. El seguimiento de los puntos de interés relacionados a cada tarea se realiza empleando lazos de control proporcional para cada agente identificando los puntos de ruta dentro de la línea de visión y considerando rutas optimizadas tomando la estimaciói brindada por la métrica de distancia por inundación 8NF. Adicionalmente se presentó un algoritmo de reconfiguración de la red heurístico que permite cambiar la topología de la red manteniendo la conectividad de la misma para cada instante de tiempo. Este marco de trabajo completo permite a un equipo de robots autónomos brindar asistencia valiosa en ciertas situaciones de búsqueda y rescate d´onde los equipos humanos sean insuficientes y/o las condiciones de la misión pueden ser peligrosas para las personas teniendo en cuenta que si bien los robots actualmente no son capaces de realizar tareas paramédicas si son capaces de realizar múltiples tareas útiles para aligerar el trabajo y el riesgo para equipos humanos en estas situaciones. El funcionamiento de estos algoritmos es presentado en simulaciones no triviales en Matlab R buscando presentar los comportamientos que emergen en los robots y adicionalmente fue implementado en una versión simplificada con robots móviles tipo turtlebot y configuraciones simples de robots BioloidMaestrí
Selected papers on Hands-on Science II
This second volume of the "Selected Papers on Hands-on Science" the Hands-on Science Network is publishing, reunites some of the most relevant works presented at the 2008, 2009, 2010 and 2011 editions of the annual International Conference on Hands-on Science. From pre-school science education to lifelong science learning and teacher training, in formal non-formal and informal contexts, the large diversified range of works that conforms this book surely renders it an important tool to schools and educators and all involved in science education and on the promotion of scientific literacy.info:eu-repo/semantics/publishedVersio
Evaluation in artificial intelligence: From task-oriented to ability-oriented measurement
The final publication is available at Springer via http://dx.doi.org/ 10.1007/s10462-016-9505-7.The evaluation of artificial intelligence systems and components is crucial for the
progress of the discipline. In this paper we describe and critically assess the different ways
AI systems are evaluated, and the role of components and techniques in these systems. We
first focus on the traditional task-oriented evaluation approach. We identify three kinds of
evaluation: human discrimination, problem benchmarks and peer confrontation. We describe
some of the limitations of the many evaluation schemes and competitions in these three categories,
and follow the progression of some of these tests. We then focus on a less customary
(and challenging) ability-oriented evaluation approach, where a system is characterised by
its (cognitive) abilities, rather than by the tasks it is designed to solve. We discuss several
possibilities: the adaptation of cognitive tests used for humans and animals, the development
of tests derived from algorithmic information theory or more integrated approaches under
the perspective of universal psychometrics. We analyse some evaluation tests from AI that
are better positioned for an ability-oriented evaluation and discuss how their problems and
limitations can possibly be addressed with some of the tools and ideas that appear within
the paper. Finally, we enumerate a series of lessons learnt and generic guidelines to be used
when an AI evaluation scheme is under consideration.I thank the organisers of the AEPIA Summer School On Artificial Intelligence, held in September 2014, for giving me the opportunity to give a lecture on 'AI Evaluation'. This paper was born out of and evolved through that lecture. The information about many benchmarks and competitions discussed in this paper have been contrasted with information from and discussions with many people: M. Bedia, A. Cangelosi, C. Dimitrakakis, I. GarcIa-Varea, Katja Hofmann, W. Langdon, E. Messina, S. Mueller, M. Siebers and C. Soares. Figure 4 is courtesy of F. Martinez-Plumed. Finally, I thank the anonymous reviewers, whose comments have helped to significantly improve the balance and coverage of the paper. This work has been partially supported by the EU (FEDER) and the Spanish MINECO under Grants TIN 2013-45732-C4-1-P, TIN 2015-69175-C4-1-R and by Generalitat Valenciana PROMETEOII2015/013.José Hernández-Orallo (2016). 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Automated Hierarchical, Forward-Chaining Temporal Planner for Planetary Robots Exploring Unknown Environments
The transition of mobile robots from a controlled environment towards the real-world represents a major leap in terms of complexity coming primarily from three different factors: partial observability, nondeterminism and dynamic events. To cope with them, robots must achieve some intelligence behaviours to be cost and operationally effective.
Two particularly interesting examples of highly complex robotic scenarios are Mars rover missions and the Darpa Robotic Challenge (DRC). In spite of the important differences they present in terms of constraints and requirements, they both have adopted certain level of autonomy to overcome some specific problems. For instance, Mars rovers have been endowed with multiple systems to enable autonomous payload operations and consequently increase science return. In the case of DRC, most teams have autonomous footstep planning or arm trajectory calculation.
Even though some specific problems can be addressed with dedicated tools, the general problem remains unsolved: to deploy on-board a reliable reasoning system able to operate robots without human intervention even in complex environments. This is precisely the goal of an automated mission planner.
The scientific community has provided plenty of planners able to provide very fast solutions for classical problems, typically characterized by the lack of time and resources representation. Moreover, there are also a handful of applied planners with higher levels of expressiveness at the price of lowest performance. However, a fast, expressive and robust planner has never been used in complex robotic missions. These three properties represent the main drivers for the outcomes of the thesis.
To bridge the gap between classical and applied planning, a novel formalism named Hierarchical TimeLine Networks (HTLN) combining Timeline and HTN planning has been proposed. HTLN has been implemented on a mission planner named QuijoteExpress, the first forward-chaining timeline planner to the best of our knowledge. The main idea is to benefit from the great performance of forward-chaining search to resolve temporal problems on the state-space. In addition, QuijoteExpress includes search enhancements such as parallel planning by division of the problem in sub-problems or
advanced heuristics management. Regarding expressiveness, the planner incorporates HTN techniques that allow to define hierarchical models and solutions. Finally, plan robustness in uncertain scenarios has been addressed by means of sufficient plans that allow to leave parts of valid plans undefined.
To test the planner, a novel lightweight, timeline and ROS-based executive named SanchoExpress has been designed to translate the plans into actions understandable by the different robot subsystems.
The entire approach has been tested in two realistic and complementary domains. A cooperative multirover Mars mission and an urban search and rescue mission. The results were extremely positive and opens new promising ways in the field of automated planning applied to robotics
Scaled Autonomy for Networked Humanoids
Humanoid robots have been developed with the intention of aiding in environments designed for humans. As such, the control of humanoid morphology and effectiveness of human robot interaction form the two principal research issues for deploying these robots in the real world. In this thesis work, the issue of humanoid control is coupled with human robot interaction under the framework of scaled autonomy, where the human and robot exchange levels of control depending on the environment and task at hand. This scaled autonomy is approached with control algorithms for reactive stabilization of human commands and planned trajectories that encode semantically meaningful motion preferences in a sequential convex optimization framework.
The control and planning algorithms have been extensively tested in the field for robustness and system verification. The RoboCup competition provides a benchmark competition for autonomous agents that are trained with a human supervisor. The kid-sized and adult-sized humanoid robots coordinate over a noisy network in a known environment with adversarial opponents, and the software and routines in this work allowed for five consecutive championships. Furthermore, the motion planning and user interfaces developed in the work have been tested in the noisy network of the DARPA Robotics Challenge (DRC) Trials and Finals in an unknown environment.
Overall, the ability to extend simplified locomotion models to aid in semi-autonomous manipulation allows untrained humans to operate complex, high dimensional robots. This represents another step in the path to deploying humanoids in the real world, based on the low dimensional motion abstractions and proven performance in real world tasks like RoboCup and the DRC