Motion planning using synergies : application to anthropomorphic dual-arm robots

Motion planning is a traditional field in robotics, but new problems are nevertheless incessantly appearing, due to continuous advances in the robot developments. In order to solve these new problems, as well as to improve the existing solutions to classical problems, new approaches are being proposed. A paradigmatic case is the humanoid robotics, since the advances done in this field require motion planners not only to look efficiently for an optimal solution in the classic way, i.e. optimizing consumed energy or time in the plan execution, but also looking for human-like solutions, i.e. requiring the robot movements to be similar to those of the human beings. This anthropomorphism in the robot motion is desired not only for aesthetical reasons, but it is also needed to allow a better and safer human-robot collaboration: humans can predict more easily anthropomorphic robot motions thus avoiding collisions and enhancing the collaboration with the robot. Nevertheless, obtaining a satisfactory performance of these anthropomorphic robotic systems requires the automatic planning of the movements, which is still an arduous and non-evident task since the complexity of the planning problem increases exponentially with the number of degrees of freedom of the robotic system. This doctoral thesis tackles the problem of planning the motions of dual-arm anthropomorphic robots (optionally with mobile base). The main objective is twofold: obtaining robot motions both in an efficient and in a human-like fashion at the same time. Trying to mimic the human movements while reducing the complexity of the search space for planning purposes leads to the concept of synergies, which could be conceptually defined as correlations (in the joint configuration space as well as in the joint velocity space) between the degrees of freedom of the system. This work proposes new sampling-based motion-planning procedures that exploit the concept of synergies, both in the configuration and velocity space, coordinating the movements of the arms, the hands and the mobile base of mobile anthropomorphic dual-arm robots.La planificación de movimientos es un campo tradicional de la robótica, sin embargo aparecen incesantemente nuevos problemas debido a los continuos avances en el desarrollo de los robots. Para resolver esos nuevos problemas, así como para mejorar las soluciones existentes a los problemas clásicos, se están proponiendo nuevos enfoques. Un caso paradigmático es la robótica humanoide, ya que los avances realizados en este campo requieren que los algoritmos planificadores de movimientos no sólo encuentren eficientemente una solución óptima en el sentido clásico, es decir, optimizar el consumo de energía o el tiempo de ejecución de la trayectoria; sino que también busquen soluciones con apariencia humana, es decir, que el movimiento del robot sea similar al del ser humano. Este antropomorfismo en el movimiento del robot se busca no sólo por razones estéticas, sino porque también es necesario para permitir una colaboración mejor y más segura entre el robot y el operario: el ser humano puede predecir con mayor facilidad los movimientos del robot si éstos son antropomórficos, evitando así las colisiones y mejorando la colaboración humano robot. Sin embargo, para obtener un desempeño satisfactorio de estos sistemas robóticos antropomórficos se requiere una planificación automática de sus movimientos, lo que sigue siendo una tarea ardua y poco evidente, ya que la complejidad del problema aumenta exponencialmente con el número de grados de libertad del sistema robótico. Esta tesis doctoral aborda el problema de la planificación de movimientos en robots antropomorfos bibrazo (opcionalmente con base móvil). El objetivo aquí es doble: obtener movimientos robóticos de forma eficiente y, a la vez, que tengan apariencia humana. Intentar imitar los movimientos humanos mientras a la vez se reduce la complejidad del espacio de búsqueda conduce al concepto de sinergias, que podrían definirse conceptualmente como correlaciones (tanto en el espacio de configuraciones como en el espacio de velocidades de las articulaciones) entre los distintos grados de libertad del sistema. Este trabajo propone nuevos procedimientos de planificación de movimientos que explotan el concepto de sinergias, tanto en el espacio de configuraciones como en el espacio de velocidades, coordinando así los movimientos de los brazos, las manos y la base móvil de robots móviles, bibrazo y antropomórficos.Postprint (published version

A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

Machine Performers: Agents in a Multiple Ontological State

In this thesis, the author explores and develops new attributes for machine performers and merges the trans-disciplinary fields of the performing arts and artificial intelligence. The main aim is to redefine the term “embodiment” for robots on the stage and to demonstrate that this term requires broadening in various fields of research. This redefining has required a multifaceted theoretical analysis of embodiment in the field of artificial intelligence (e.g. the uncanny valley), as well as the construction of new robots for the stage by the author. It is hoped that these practical experimental examples will generate more research by others in similar fields. Even though the historical lineage of robotics is engraved with theatrical strategies and dramaturgy, further application of constructive principles from the performing arts and evidence from psychology and neurology can shift the perception of robotic agents both on stage and in other cultural environments. In this light, the relation between representation, movement and behaviour of bodies has been further explored to establish links between constructed bodies (as in artificial intelligence) and perceived bodies (as performers on the theatrical stage). In the course of this research, several practical works have been designed and built, and subsequently presented to live audiences and research communities. Audience reactions have been analysed with surveys and discussions. Interviews have also been conducted with choreographers, curators and scientists about the value of machine performers. The main conclusions from this study are that fakery and mystification can be used as persuasive elements to enhance agency. Morphologies can also be applied that tightly couple brain and sensorimotor actions and lead to a stronger stage presence. In fact, if this lack of presence is left out of human replicants, it causes an “uncanny” lack of agency. Furthermore, the addition of stage presence leads to stronger identification from audiences, even for bodies dissimilar to their own. The author demonstrates that audience reactions are enhanced by building these effects into machine body structures: rather than identification through mimicry, this causes them to have more unambiguously biological associations. Alongside these traits, atmospheres such as those created by a cast of machine performers tend to cause even more intensely visceral responses. In this thesis, “embodiment” has emerged as a paradigm shift – as well as within this shift – and morphological computing has been explored as a method to deepen this visceral immersion. Therefore, this dissertation considers and builds machine performers as “true” performers for the stage, rather than mere objects with an aura. Their singular and customized embodiment can enable the development of non-anthropocentric performances that encompass the abstract and conceptual patterns in motion and generate – as from human performers – empathy, identification and experiential reactions in live audiences

Data efficiency in imitation learning with a focus on object manipulation

Imitation is a natural human behaviour that helps us learn new skills. Modelling this behaviour in robots, however, has many challenges. This thesis investigates the challenge of handling the expert demonstrations in an efficient way, so as to minimise the number of demonstrations required for robots to learn. To achieve this, it focuses on demonstration data efficiency at various steps of the imitation process. Specifically, it presents new methodologies that offer ways to acquire, augment and combine demonstrations in order to improve the overall imitation process. Firstly, the thesis explores an inexpensive and non-intrusive way of acquiring dexterous human demonstrations. Human hand actions are quite complex, especially when they involve object manipulation. The proposed framework tackles this by using a camera to capture the hand information and then retargeting it to a dexterous hand model. It does this by combining inverse kinematics with stochastic optimisation. The demonstrations collected with this framework can then be used in the imitation process. Secondly, the thesis presents a novel way to apply data augmentation to demonstrations. The main difficulty of augmenting demonstrations is that their trajectorial nature can make them unsuccessful. Whilst previous works require additional knowledge about the task or demonstrations to achieve this, this method performs augmentation automatically. To do this, it introduces a correction network that corrects the augmentations based on the distribution of the original experts. Lastly, the thesis investigates data efficiency in a multi-task scenario where it additionally proposes a data combination method. Its aim is to automatically divide a set of tasks into sub-behaviours. Contrary to previous works, it does this without any additional knowledge about the tasks. To achieve this, it uses both task-specific and shareable modules. This minimises negative transfer and allows for the method to be applied to various task sets with different commonalities.Open Acces

Structured manifolds for motion production and segmentation : a structured Kernel Regression approach

Steffen JF. Structured manifolds for motion production and segmentation : a structured Kernel Regression approach. Bielefeld (Germany): Bielefeld University; 2010

Towards a framework for socially interactive robots

250 p.En las últimas décadas, la investigación en el campo de la robótica social ha crecido considerablemente. El desarrollo de diferentes tipos de robots y sus roles dentro de la sociedad se están expandiendo poco a poco. Los robots dotados de habilidades sociales pretenden ser utilizados para diferentes aplicaciones; por ejemplo, como profesores interactivos y asistentes educativos, para apoyar el manejo de la diabetes en niños, para ayudar a personas mayores con necesidades especiales, como actores interactivos en el teatro o incluso como asistentes en hoteles y centros comerciales.El equipo de investigación RSAIT ha estado trabajando en varias áreas de la robótica, en particular,en arquitecturas de control, exploración y navegación de robots, aprendizaje automático y visión por computador. El trabajo presentado en este trabajo de investigación tiene como objetivo añadir una nueva capa al desarrollo anterior, la capa de interacción humano-robot que se centra en las capacidades sociales que un robot debe mostrar al interactuar con personas, como expresar y percibir emociones, mostrar un alto nivel de diálogo, aprender modelos de otros agentes, establecer y mantener relaciones sociales, usar medios naturales de comunicación (mirada, gestos, etc.),mostrar personalidad y carácter distintivos y aprender competencias sociales.En esta tesis doctoral, tratamos de aportar nuestro grano de arena a las preguntas básicas que surgen cuando pensamos en robots sociales: (1) ¿Cómo nos comunicamos (u operamos) los humanos con los robots sociales?; y (2) ¿Cómo actúan los robots sociales con nosotros? En esa línea, el trabajo se ha desarrollado en dos fases: en la primera, nos hemos centrado en explorar desde un punto de vista práctico varias formas que los humanos utilizan para comunicarse con los robots de una maneranatural. En la segunda además, hemos investigado cómo los robots sociales deben actuar con el usuario.Con respecto a la primera fase, hemos desarrollado tres interfaces de usuario naturales que pretenden hacer que la interacción con los robots sociales sea más natural. Para probar tales interfaces se han desarrollado dos aplicaciones de diferente uso: robots guía y un sistema de controlde robot humanoides con fines de entretenimiento. Trabajar en esas aplicaciones nos ha permitido dotar a nuestros robots con algunas habilidades básicas, como la navegación, la comunicación entre robots y el reconocimiento de voz y las capacidades de comprensión.Por otro lado, en la segunda fase nos hemos centrado en la identificación y el desarrollo de los módulos básicos de comportamiento que este tipo de robots necesitan para ser socialmente creíbles y confiables mientras actúan como agentes sociales. Se ha desarrollado una arquitectura(framework) para robots socialmente interactivos que permite a los robots expresar diferentes tipos de emociones y mostrar un lenguaje corporal natural similar al humano según la tarea a realizar y lascondiciones ambientales.La validación de los diferentes estados de desarrollo de nuestros robots sociales se ha realizado mediante representaciones públicas. La exposición de nuestros robots al público en esas actuaciones se ha convertido en una herramienta esencial para medir cualitativamente la aceptación social de los prototipos que estamos desarrollando. De la misma manera que los robots necesitan un cuerpo físico para interactuar con el entorno y convertirse en inteligentes, los robots sociales necesitan participar socialmente en tareas reales para las que han sido desarrollados, para así poder mejorar su sociabilida

Modeling of human movement for the generation of humanoid robot motion

La robotique humanoïde arrive a maturité avec des robots plus rapides et plus précis. Pour faire face à la complexité mécanique, la recherche a commencé à regarder au-delà du cadre habituel de la robotique, vers les sciences de la vie, afin de mieux organiser le contrôle du mouvement. Cette thèse explore le lien entre mouvement humain et le contrôle des systèmes anthropomorphes tels que les robots humanoïdes. Tout d’abord, en utilisant des méthodes classiques de la robotique, telles que l’optimisation, nous étudions les principes qui sont à la base de mouvements répétitifs humains, tels que ceux effectués lorsqu’on joue au yoyo. Nous nous concentrons ensuite sur la locomotion en nous inspirant de résultats en neurosciences qui mettent en évidence le rôle de la tête dans la marche humaine. En développant une interface permettant à un utilisateur de commander la tête du robot, nous proposons une méthode de contrôle du mouvement corps-complet d’un robot humanoïde, incluant la production de pas et permettant au corps de suivre le mouvement de la tête. Cette idée est poursuivie dans l’étude finale dans laquelle nous analysons la locomotion de sujets humains, dirigée vers une cible, afin d’extraire des caractéristiques du mouvement sous forme invariants. En faisant le lien entre la notion “d’invariant” en neurosciences et celle de “tâche cinématique” en robotique humanoïde, nous développons une méthode pour produire une locomotion réaliste pour d’autres systèmes anthropomorphes. Dans ce cas, les résultats sont illustrés sur le robot humanoïde HRP2 du LAAS-CNRS. La contribution générale de cette thèse est de montrer que, bien que la planification de mouvement pour les robots humanoïdes peut être traitée par des méthodes classiques de robotique, la production de mouvements réalistes nécessite de combiner ces méthodes à l’observation systématique et formelle du comportement humain. ABSTRACT : Humanoid robotics is coming of age with faster and more agile robots. To compliment the physical complexity of humanoid robots, the robotics algorithms being developed to derive their motion have also become progressively complex. The work in this thesis spans across two research fields, human neuroscience and humanoid robotics, and brings some ideas from the former to aid the latter. By exploring the anthropological link between the structure of a human and that of a humanoid robot we aim to guide conventional robotics methods like local optimization and task-based inverse kinematics towards more realistic human-like solutions. First, we look at dynamic manipulation of human hand trajectories while playing with a yoyo. By recording human yoyo playing, we identify the control scheme used as well as a detailed dynamic model of the hand-yoyo system. Using optimization this model is then used to implement stable yoyo-playing within the kinematic and dynamic limits of the humanoid HRP-2. The thesis then extends its focus to human and humanoid locomotion. We take inspiration from human neuroscience research on the role of the head in human walking and implement a humanoid robotics analogy to this. By allowing a user to steer the head of a humanoid, we develop a control method to generate deliberative whole-body humanoid motion including stepping, purely as a consequence of the head movement. This idea of understanding locomotion as a consequence of reaching a goal is extended in the final study where we look at human motion in more detail. Here, we aim to draw to a link between “invariants” in neuroscience and “kinematic tasks” in humanoid robotics. We record and extract stereotypical characteristics of human movements during a walking and grasping task. These results are then normalized and generalized such that they can be regenerated for other anthropomorphic figures with different kinematic limits than that of humans. The final experiments show a generalized stack of tasks that can generate realistic walking and grasping motion for the humanoid HRP-2. The general contribution of this thesis is in showing that while motion planning for humanoid robots can be tackled by classical methods of robotics, the production of realistic movements necessitate the combination of these methods with the systematic and formal observation of human behavior

Imitation and social learning for synthetic characters

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2004.Includes bibliographical references (p. 137-149).We want to build animated characters and robots capable of rich social interactions with humans and each other, and who are able to learn by observing those around them. An increasing amount of evidence suggests that, in human infants, the ability to learn by watching others, and in particular, the ability to imitate, could be crucial precursors to the development of appropriate social behavior, and ultimately the ability to reason about the thoughts, intents, beliefs, and desires of others. We have created a number of imitative characters and robots, the latest of which is Max T. Mouse, an anthropomorphic animated mouse character who is able to observe the actions he sees his friend Morris Mouse performing, and compare them to the actions he knows how to perform himself. This matching process allows Max to accurately imitate Morris's gestures and actions, even when provided with limited synthetic visual input. Furthermore, by using his own perception, motor, and action systems as models for the behavioral and perceptual capabilities of others (a process known as Simulation Theory in the cognitive literature), Max can begin to identify simple goals and motivations for Morris's behavior, an important step towards developing characters with a full theory of mind. Finally, Max can learn about unfamiliar objects in his environment, such as food and toys, by observing and correctly interpreting Morris's interactions with these objects, demonstrating his ability to take advantage of socially acquired information.by Daphna Buchsbaum.S.M

Computational Methods for Cognitive and Cooperative Robotics

In the last decades design methods in control engineering made substantial progress in the areas of robotics and computer animation. Nowadays these methods incorporate the newest developments in machine learning and artificial intelligence. But the problems of flexible and online-adaptive combinations of motor behaviors remain challenging for human-like animations and for humanoid robotics. In this context, biologically-motivated methods for the analysis and re-synthesis of human motor programs provide new insights in and models for the anticipatory motion synthesis. This thesis presents the author’s achievements in the areas of cognitive and developmental robotics, cooperative and humanoid robotics and intelligent and machine learning methods in computer graphics. The first part of the thesis in the chapter “Goal-directed Imitation for Robots” considers imitation learning in cognitive and developmental robotics. The work presented here details the author’s progress in the development of hierarchical motion recognition and planning inspired by recent discoveries of the functions of mirror-neuron cortical circuits in primates. The overall architecture is capable of ‘learning for imitation’ and ‘learning by imitation’. The complete system includes a low-level real-time capable path planning subsystem for obstacle avoidance during arm reaching. The learning-based path planning subsystem is universal for all types of anthropomorphic robot arms, and is capable of knowledge transfer at the level of individual motor acts. Next, the problems of learning and synthesis of motor synergies, the spatial and spatio-temporal combinations of motor features in sequential multi-action behavior, and the problems of task-related action transitions are considered in the second part of the thesis “Kinematic Motion Synthesis for Computer Graphics and Robotics”. In this part, a new approach of modeling complex full-body human actions by mixtures of time-shift invariant motor primitives in presented. The online-capable full-body motion generation architecture based on dynamic movement primitives driving the time-shift invariant motor synergies was implemented as an online-reactive adaptive motion synthesis for computer graphics and robotics applications. The last chapter of the thesis entitled “Contraction Theory and Self-organized Scenarios in Computer Graphics and Robotics” is dedicated to optimal control strategies in multi-agent scenarios of large crowds of agents expressing highly nonlinear behaviors. This last part presents new mathematical tools for stability analysis and synthesis of multi-agent cooperative scenarios.In den letzten Jahrzehnten hat die Forschung in den Bereichen der Steuerung und Regelung komplexer Systeme erhebliche Fortschritte gemacht, insbesondere in den Bereichen Robotik und Computeranimation. Die Entwicklung solcher Systeme verwendet heutzutage neueste Methoden und Entwicklungen im Bereich des maschinellen Lernens und der künstlichen Intelligenz. Die flexible und echtzeitfähige Kombination von motorischen Verhaltensweisen ist eine wesentliche Herausforderung für die Generierung menschenähnlicher Animationen und in der humanoiden Robotik. In diesem Zusammenhang liefern biologisch motivierte Methoden zur Analyse und Resynthese menschlicher motorischer Programme neue Erkenntnisse und Modelle für die antizipatorische Bewegungssynthese. Diese Dissertation präsentiert die Ergebnisse der Arbeiten des Autors im Gebiet der kognitiven und Entwicklungsrobotik, kooperativer und humanoider Robotersysteme sowie intelligenter und maschineller Lernmethoden in der Computergrafik. Der erste Teil der Dissertation im Kapitel “Zielgerichtete Nachahmung für Roboter” behandelt das Imitationslernen in der kognitiven und Entwicklungsrobotik. Die vorgestellten Arbeiten beschreiben neue Methoden für die hierarchische Bewegungserkennung und -planung, die durch Erkenntnisse zur Funktion der kortikalen Spiegelneuronen-Schaltkreise bei Primaten inspiriert wurden. Die entwickelte Architektur ist in der Lage, ‘durch Imitation zu lernen’ und ‘zu lernen zu imitieren’. Das komplette entwickelte System enthält ein echtzeitfähiges Pfadplanungssubsystem zur Hindernisvermeidung während der Durchführung von Armbewegungen. Das lernbasierte Pfadplanungssubsystem ist universell und für alle Arten von anthropomorphen Roboterarmen in der Lage, Wissen auf der Ebene einzelner motorischer Handlungen zu übertragen. Im zweiten Teil der Arbeit “Kinematische Bewegungssynthese für Computergrafik und Robotik” werden die Probleme des Lernens und der Synthese motorischer Synergien, d.h. von räumlichen und räumlich-zeitlichen Kombinationen motorischer Bewegungselemente bei Bewegungssequenzen und bei aufgabenbezogenen Handlungs übergängen behandelt. Es wird ein neuer Ansatz zur Modellierung komplexer menschlicher Ganzkörperaktionen durch Mischungen von zeitverschiebungsinvarianten Motorprimitiven vorgestellt. Zudem wurde ein online-fähiger Synthesealgorithmus für Ganzköperbewegungen entwickelt, der auf dynamischen Bewegungsprimitiven basiert, die wiederum auf der Basis der gelernten verschiebungsinvarianten Primitive konstruiert werden. Dieser Algorithmus wurde für verschiedene Probleme der Bewegungssynthese für die Computergrafik- und Roboteranwendungen implementiert. Das letzte Kapitel der Dissertation mit dem Titel “Kontraktionstheorie und selbstorganisierte Szenarien in der Computergrafik und Robotik” widmet sich optimalen Kontrollstrategien in Multi-Agenten-Szenarien, wobei die Agenten durch eine hochgradig nichtlineare Kinematik gekennzeichnet sind. Dieser letzte Teil präsentiert neue mathematische Werkzeuge für die Stabilitätsanalyse und Synthese von kooperativen Multi-Agenten-Szenarien

Review of the techniques used in motor‐cognitive human‐robot skill transfer

Abstract A conventional robot programming method extensively limits the reusability of skills in the developmental aspect. Engineers programme a robot in a targeted manner for the realisation of predefined skills. The low reusability of general‐purpose robot skills is mainly reflected in inability in novel and complex scenarios. Skill transfer aims to transfer human skills to general‐purpose manipulators or mobile robots to replicate human‐like behaviours. Skill transfer methods that are commonly used at present, such as learning from demonstrated (LfD) or imitation learning, endow the robot with the expert's low‐level motor and high‐level decision‐making ability, so that skills can be reproduced and generalised according to perceived context. The improvement of robot cognition usually relates to an improvement in the autonomous high‐level decision‐making ability. Based on the idea of establishing a generic or specialised robot skill library, robots are expected to autonomously reason about the needs for using skills and plan compound movements according to sensory input. In recent years, in this area, many successful studies have demonstrated their effectiveness. Herein, a detailed review is provided on the transferring techniques of skills, applications, advancements, and limitations, especially in the LfD. Future research directions are also suggested