Plan Projection, Execution, and Learning for Mobile Robot Control

Most state-of-the-art hybrid control systems for mobile robots are decomposed into different layers. While the deliberation layer reasons about the actions required for the robot in order to achieve a given goal, the behavioral layer is designed to enable the robot to quickly react to unforeseen events. This decomposition guarantees a safe operation even in the presence of unforeseen and dynamic obstacles and enables the robot to cope with situations it was not explicitly programmed for. The layered design, however, also leaves us with the problem of plan execution. The problem of plan execution is the problem of arbitrating between the deliberation- and the behavioral layer. Abstract symbolic actions have to be translated into streams of local control commands. Simultaneously, execution failures have to be handled on an appropriate level of abstraction. It is now widely accepted that plan execution should form a third layer of a hybrid robot control system. The resulting layered architectures are called three-tiered architectures, or 3T architectures for short. Although many high level programming frameworks have been proposed to support the implementation of the intermediate layer, there is no generally accepted algorithmic basis for plan execution in three-tiered architectures. In this thesis, we propose to base plan execution on plan projection and learning and present a general framework for the self-supervised improvement of plan execution. This framework has been implemented in APPEAL, an Architecture for Plan Projection, Execution And Learning, which extends the well known RHINO control system by introducing an execution layer. This thesis contributes to the field of plan-based mobile robot control which investigates the interrelation between planning, reasoning, and learning techniques based on an explicit representation of the robot's intended course of action, a plan. In McDermott's terminology, a plan is that part of a robot control program, which the robot cannot only execute, but also reason about and manipulate. According to that broad view, a plan may serve many purposes in a robot control system like reasoning about future behavior, the revision of intended activities, or learning. In this thesis, plan-based control is applied to the self-supervised improvement of mobile robot plan execution

Goal Reasoning: Papers from the ACS workshop

This technical report contains the 11 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2013 Conference on Advances in Cognitive Systems (ACS-13) in Baltimore, Maryland on 14 December 2013. This is the third in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy while the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012. Our objective for holding this meeting was to encourage researchers to share information on the study, development, integration, evaluation, and application of techniques related to goal reasoning, which concerns the ability of an intelligent agent to reason about, formulate, select, and manage its goals/objectives. Goal reasoning differs from frameworks in which agents are told what goals to achieve, and possibly how goals can be decomposed into subgoals, but not how to dynamically and autonomously decide what goals they should pursue. This constraint can be limiting for agents that solve tasks in complex environments when it is not feasible to manually engineer/encode complete knowledge of what goal(s) should be pursued for every conceivable state. Yet, in such environments, states can be reached in which actions can fail, opportunities can arise, and events can otherwise take place that strongly motivate changing the goal(s) that the agent is currently trying to achieve. This topic is not new; researchers in several areas have studied goal reasoning (e.g., in the context of cognitive architectures, automated planning, game AI, and robotics). However, it has infrequently been the focus of intensive study, and (to our knowledge) no other series of meetings has focused specifically on goal reasoning. As shown in these papers, providing an agent with the ability to reason about its goals can increase performance measures for some tasks. Recent advances in hardware and software platforms (involving the availability of interesting/complex simulators or databases) have increasingly permitted the application of intelligent agents to tasks that involve partially observable and dynamically-updated states (e.g., due to unpredictable exogenous events), stochastic actions, multiple (cooperating, neutral, or adversarial) agents, and other complexities. Thus, this is an appropriate time to foster dialogue among researchers with interests in goal reasoning. Research on goal reasoning is still in its early stages; no mature application of it yet exists (e.g., for controlling autonomous unmanned vehicles or in a deployed decision aid). However, it appears to have a bright future. For example, leaders in the automated planning community have specifically acknowledged that goal reasoning has a prominent role among intelligent agents that act on their own plans, and it is gathering increasing attention from roboticists and cognitive systems researchers. In addition to a survey, the papers in this workshop relate to, among other topics, cognitive architectures and models, environment modeling, game AI, machine learning, meta-reasoning, planning, selfmotivated systems, simulation, and vehicle control. The authors discuss a wide range of issues pertaining to goal reasoning, including representations and reasoning methods for dynamically revising goal priorities. We hope that readers will find that this theme for enhancing agent autonomy to be appealing and relevant to their own interests, and that these papers will spur further investigations on this important yet (mostly) understudied topic

Sequence Planner: A Framework for Control of Intelligent Automation Systems

This paper presents a framework that tackles the challenges met in the development of automation systems featuring collaborative robotics and other machines that have some degree of autonomy. These machines rely on online algorithms for both sensing and acting in order to achieve a very high level of flexibility. To take advantage of these new machines and algorithms, control systems must also be increasingly flexible. In this paper, we present a framework for control of this new class of intelligent automation systems called Sequence Planner (SP), which helps with control of both traditional automation equipment and machines with autonomy. To aid the complex task of developing automation control solutions, SP relies on supporting algorithms for control logic synthesis and online planning. SP has been implemented with plug-in support for the Robot Operating System (ROS) and applied to an industrial demonstrator. We present our findings on how SP performed as a control system for this demonstrator, where we show that it is an adequate approach to implement automation for a highly flexible single station system. As a standardized way of automating such systems is missing, we hope that our contribution will provide a foundation for how to develop intelligent automation systems

Longterm Generalized Actions for Smart, Autonomous Robot Agents

Creating intelligent artificial systems, and in particular robots, that improve themselves just like humans do is one of the most ambitious goals in robotics and machine learning. The concept of robot experience exists for some time now, but has up to now not fully found its way into autonomous robots. This thesis is devoted to both, analyzing the underlying requirements for enabling robot learning from experience and actually implementing it on real robot hardware. For effective robot learning from experience I present and discuss three main requirements: (a ) Clearly expressing what a robot should do, on a vague, abstract level I introduce Generalized Plans as a means to express the intention rather than the actual action sequence of a task, removing as much task specific knowledge as possible. (a ) Defining, collecting, and analyzing robot experiences to enable robots to improve I present Episodic Memories as a container for all collected robot experiences for any arbitrary task and create sophisticated action (effect) prediction models from them, allowing robots to make better decisions. (a ) Properly abstracting from reality and dealing with failures in the domain they occurred in I propose failure handling strategies, a failure taxonomy extensible through experience, and discuss the relationship between symbolic/discrete and subsymbolic/continuous systems in terms of robot plans interacting with real world sensors and actuators. I concentrate on the domain of human-scale robot activities, specifically on doing household chores. Tasks in this domain offer many repeating patterns and are ideal candidates for abstracting, encapsulating, and modularizing robot plans into a more general form. This way, very similar plan structures are transformed into parameters that change the behavior of the robot while performing the task, making the plans more flexible. While performing tasks, robots encounter the same or similar situations over and over again. Albeit humans are able to benefit from this and improve at what they do, robots in general lack this ability. This thesis presents techniques for collecting and making robot experiences accessible to robots and outside observers alike, answering high level questions such as What are good spots to stand at for grasping objects from the fridge? or Which objects are especially difficult to grasp with two hands while they are in the oven? . By structuring and tapping into a robot's memory, it can make more informed decisions that are not based on manually encoded information, but self-improved behavior. To this end, I present several experience-based approaches to improve a robot's autonomous decisions, such as parameter choices, during execution time. Robots that interact with the real world are bound to deal with unexpected events and must properly react to failures of any kind of action. I present an extensible failure model that suits the structure of Generalized Plans and Episodic Memories and make clear how each module should deal with their own failures rather than directly handing them up to a governing cognitive architecture. In addition, I make a distinction between discrete parametrizations of Generalized Plans and continuous low level components, and how to translate between the two

Adapting robot behavior to user preferences in assistive scenarios

Aplicat embargament des de la data de defensa fins el 24 de juliol de 2020Robotic assistants have inspired numerous books and science fiction movies. In the real world, these kinds of devices are a growing need in amongst the elderly, who while life continue requiring more assistance. While life expectancy is increasing, life quality is not necessarily doing so. Thus, we may find ourselves and our loved ones being dependent and needing another person to perform the most basic tasks, which has a strong psychological impact. Accordingly, assistive robots may be the definitive tool to give more quality of life by empowering dependent people and extending their independent living. Assisting users to perform daily activities requires adapting to them and their needs, as they might not be able to adapt to the robot. This thesis tackles adaptation and personalization issues through user preferences. We 'focus on physical tasks that involve close contact, as these present interesting challenges, and are of great importance for he user. Therefore, three tasks are mainly used throughout the thesis: assistive feeding, shoe fitting, and jacket dressing. We first describe a framework for robot behavior adaptation that illustrates how robots should be personalized for and by end- users or their assistants. Using this framework, non-technical users determine how !he robot should behave. Then, we define the concept of preference for assistive robotics scenarios and establish a taxonomy, which includes hierarchies and groups of preferences, grounding definitions and concepts. We then show how the preferences in the taxonomy are used with Al planning systems to adapt the robot behavior to the preferences of the user obtained from simple questions. Our algorithms allow for long-term adaptations as well as to cope with misinformed user models. We further integrate the methods with low-level motion primitives that provide a more robust adaptation and behavior while lowering the number of needed actions and demonstrations. Moreover, we perform a deeper analysis in Planning and preferences with the introduction of new algorithms to provide preference suggestions in planning domains. The thesis then concludes with a user study that evaluates the use of the preferences in the three real assistive robotics scenarios. The experiments show a clear understanding of the preferences of users, who were able to assess the impact of their preferences on the behavior of the robot. In summary, we provide tools and algorithms to design the robotic assistants of the future. Assistants that should be able to adapt to the assisted user needs and preferences, just as human assistants do nowadays.Els assistents robòtics han inspirat nombrosos llibres i pel·lícules de ciència-ficció al llarg de la història. Però tornant al món real, aquest tipus de dispositius s'estan tornant una necessitat per a una societat que envelleix a un ritme ràpid i que, per tant, requerirà més i més assistència. Mentre l'esperança de vida augmenta, la qualitat de vida no necessàriament ho fa. Per tant, ens podem trobar a nosaltres mateixos i als nostres estimats en una situació de dependència, necessitant una altra persona per poder fer les tasques més bàsiques, cosa que té un gran impacte psicològic. En conseqüència, els robots assistencials poden ser l'eina definitiva per proporcionar una millor qualitat de vida empoderant els usuaris i allargant la seva capacitat de viure independentment. L'assistència a persones per realitzar tasques diàries requereix adaptar-se a elles i les seves necessitats, donat que aquests usuaris no poden adaptar-se al robot. En aquesta tesi, abordem el problema de l'adaptació i la personalització d'un robot mitjançant preferències de l'usuari. Ens centrem en tasques físiques, que involucren contacte amb la persona, per les seves dificultats i importància per a l'usuari. Per aquest motiu, la tesi utilitzarà principalment tres tasques com a exemple: donar menjar, posar una sabata i vestir una jaqueta. Comencem definint un marc (framework) per a la personalització del comportament del robot que defineix com s'han de personalitzar els robots per usuaris i pels seus assistents. Amb aquest marc, usuaris sense coneixements tècnics són capaços de definir com s'ha de comportar el robot. Posteriorment definim el concepte de preferència per a robots assistencials i establim una taxonomia que inclou jerarquies i grups de preferències, els quals fonamenten les definicions i conceptes. Després mostrem com les preferències de la taxonomia s'utilitzen amb sistemes planificadors amb IA per adaptar el comportament del robot a les preferències de l'usuari, que s'obtenen mitjançant preguntes simples. Els nostres algorismes permeten l'adaptació a llarg termini, així com fer front a models d'usuari mal inferits. Aquests mètodes són integrats amb primitives a baix nivell que proporcionen una adaptació i comportament més robusts a la mateixa vegada que disminueixen el nombre d'accions i demostracions necessàries. També fem una anàlisi més profunda de l'ús de les preferències amb planificadors amb la introducció de nous algorismes per fer suggeriments de preferències en dominis de planificació. La tesi conclou amb un estudi amb usuaris que avalua l'ús de les preferències en les tres tasques assistencials. Els experiments demostren un clar enteniment de les preferències per part dels usuaris, que van ser capaços de discernir quan les seves preferències eren utilitzades. En resum, proporcionem eines i algorismes per dissenyar els assistents robòtics del futur. Uns assistents que haurien de ser capaços d'adaptar-se a les preferències i necessitats de l'usuari que assisteixen, tal com els assistents humans fan avui en dia.Postprint (published version

Adapting robot behavior to user preferences in assistive scenarios

Robotic assistants have inspired numerous books and science fiction movies. In the real world, these kinds of devices are a growing need in amongst the elderly, who while life continue requiring more assistance. While life expectancy is increasing, life quality is not necessarily doing so. Thus, we may find ourselves and our loved ones being dependent and needing another person to perform the most basic tasks, which has a strong psychological impact. Accordingly, assistive robots may be the definitive tool to give more quality of life by empowering dependent people and extending their independent living. Assisting users to perform daily activities requires adapting to them and their needs, as they might not be able to adapt to the robot. This thesis tackles adaptation and personalization issues through user preferences. We 'focus on physical tasks that involve close contact, as these present interesting challenges, and are of great importance for he user. Therefore, three tasks are mainly used throughout the thesis: assistive feeding, shoe fitting, and jacket dressing. We first describe a framework for robot behavior adaptation that illustrates how robots should be personalized for and by end- users or their assistants. Using this framework, non-technical users determine how !he robot should behave. Then, we define the concept of preference for assistive robotics scenarios and establish a taxonomy, which includes hierarchies and groups of preferences, grounding definitions and concepts. We then show how the preferences in the taxonomy are used with Al planning systems to adapt the robot behavior to the preferences of the user obtained from simple questions. Our algorithms allow for long-term adaptations as well as to cope with misinformed user models. We further integrate the methods with low-level motion primitives that provide a more robust adaptation and behavior while lowering the number of needed actions and demonstrations. Moreover, we perform a deeper analysis in Planning and preferences with the introduction of new algorithms to provide preference suggestions in planning domains. The thesis then concludes with a user study that evaluates the use of the preferences in the three real assistive robotics scenarios. The experiments show a clear understanding of the preferences of users, who were able to assess the impact of their preferences on the behavior of the robot. In summary, we provide tools and algorithms to design the robotic assistants of the future. Assistants that should be able to adapt to the assisted user needs and preferences, just as human assistants do nowadays.Els assistents robòtics han inspirat nombrosos llibres i pel·lícules de ciència-ficció al llarg de la història. Però tornant al món real, aquest tipus de dispositius s'estan tornant una necessitat per a una societat que envelleix a un ritme ràpid i que, per tant, requerirà més i més assistència. Mentre l'esperança de vida augmenta, la qualitat de vida no necessàriament ho fa. Per tant, ens podem trobar a nosaltres mateixos i als nostres estimats en una situació de dependència, necessitant una altra persona per poder fer les tasques més bàsiques, cosa que té un gran impacte psicològic. En conseqüència, els robots assistencials poden ser l'eina definitiva per proporcionar una millor qualitat de vida empoderant els usuaris i allargant la seva capacitat de viure independentment. L'assistència a persones per realitzar tasques diàries requereix adaptar-se a elles i les seves necessitats, donat que aquests usuaris no poden adaptar-se al robot. En aquesta tesi, abordem el problema de l'adaptació i la personalització d'un robot mitjançant preferències de l'usuari. Ens centrem en tasques físiques, que involucren contacte amb la persona, per les seves dificultats i importància per a l'usuari. Per aquest motiu, la tesi utilitzarà principalment tres tasques com a exemple: donar menjar, posar una sabata i vestir una jaqueta. Comencem definint un marc (framework) per a la personalització del comportament del robot que defineix com s'han de personalitzar els robots per usuaris i pels seus assistents. Amb aquest marc, usuaris sense coneixements tècnics són capaços de definir com s'ha de comportar el robot. Posteriorment definim el concepte de preferència per a robots assistencials i establim una taxonomia que inclou jerarquies i grups de preferències, els quals fonamenten les definicions i conceptes. Després mostrem com les preferències de la taxonomia s'utilitzen amb sistemes planificadors amb IA per adaptar el comportament del robot a les preferències de l'usuari, que s'obtenen mitjançant preguntes simples. Els nostres algorismes permeten l'adaptació a llarg termini, així com fer front a models d'usuari mal inferits. Aquests mètodes són integrats amb primitives a baix nivell que proporcionen una adaptació i comportament més robusts a la mateixa vegada que disminueixen el nombre d'accions i demostracions necessàries. També fem una anàlisi més profunda de l'ús de les preferències amb planificadors amb la introducció de nous algorismes per fer suggeriments de preferències en dominis de planificació. La tesi conclou amb un estudi amb usuaris que avalua l'ús de les preferències en les tres tasques assistencials. Els experiments demostren un clar enteniment de les preferències per part dels usuaris, que van ser capaços de discernir quan les seves preferències eren utilitzades. En resum, proporcionem eines i algorismes per dissenyar els assistents robòtics del futur. Uns assistents que haurien de ser capaços d'adaptar-se a les preferències i necessitats de l'usuari que assisteixen, tal com els assistents humans fan avui en dia