A Continuous Grasp Representation for the Imitation Learning of Grasps on Humanoid Robots

Models and methods are presented which enable a humanoid robot to learn reusable, adaptive grasping skills. Mechanisms and principles in human grasp behavior are studied. The findings are used to develop a grasp representation capable of retaining specific motion characteristics and of adapting to different objects and tasks. Based on the representation a framework is proposed which enables the robot to observe human grasping, learn grasp representations, and infer executable grasping actions

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

Human-like arm motion generation: a review

In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analyzed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioral and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations.FCT Project UID/MAT/00013/2013FCT–Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Geometry-aware Manipulability Learning, Tracking and Transfer

Body posture influences human and robots performance in manipulation tasks, as appropriate poses facilitate motion or force exertion along different axes. In robotics, manipulability ellipsoids arise as a powerful descriptor to analyze, control and design the robot dexterity as a function of the articulatory joint configuration. This descriptor can be designed according to different task requirements, such as tracking a desired position or apply a specific force. In this context, this paper presents a novel \emph{manipulability transfer} framework, a method that allows robots to learn and reproduce manipulability ellipsoids from expert demonstrations. The proposed learning scheme is built on a tensor-based formulation of a Gaussian mixture model that takes into account that manipulability ellipsoids lie on the manifold of symmetric positive definite matrices. Learning is coupled with a geometry-aware tracking controller allowing robots to follow a desired profile of manipulability ellipsoids. Extensive evaluations in simulation with redundant manipulators, a robotic hand and humanoids agents, as well as an experiment with two real dual-arm systems validate the feasibility of the approach.Comment: Accepted for publication in the Intl. Journal of Robotics Research (IJRR). Website: https://sites.google.com/view/manipulability. Code: https://github.com/NoemieJaquier/Manipulability. 24 pages, 20 figures, 3 tables, 4 appendice

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

The Development of a Sensitive Manipulation Platform

This thesis presents an extension of sensitive manipulation which transforms tactile sensors away from end effectors and closer to whole body sensory feedback. Sensitive manipulation is a robotics concept which more closely replicates nature by employing tactile sensing to interact with the world. While traditional robotic arms are specifically designed to avoid contact, biological systems actually embrace and intentionally contact the environment. This arm is inspired by these biological systems and therefore has compliant joints and a tactile shell surrounding the two primary links of the arm. The manipulator has also been designed to be capable of both industrial and humanoid style manipulation. There are an untold number of applications for an arm with increased tactile feedback primarily in dynamic environments such as in industrial, humanoid, and prosthetic applications. The arm developed for this thesis is intended to be a desktop research platform, however, one of the most influential applications for increased tactile feedback is in prosthetics which are operate in ever changing and contact ridden environments while continuously interacting with humans. This thesis details the simulation, design, analysis, and evaluation of a the first four degrees of freedom of a robotic arm with particular attention given to the design of modular series elastic actuators in each joint as well as the incorporation of a shell of tactile sensors

The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book

A Stability-Estimator to Unify Humanoid Locomotion: Walking, Stair-Climbing and Ladder-Climbing

The field of Humanoid robotics research has often struggled to find a unique niche that is not better served by other forms of robot. Unlike more traditional industrials robots with a specific purpose, a humanoid robot is not necessarily optimized for any particular task, due to the complexity and balance issues of being bipedal. However, the versatility of a humanoid robot may be ideal for applications such as search and rescue. Disaster sites with chemical, biological, or radiation contamination mean that human rescue workers may face untenable risk. Using a humanoid robot in these dangerous circumstances could make emergency response faster and save human lives. Despite the many successes of existing mobile robots in search and rescue, stair and ladder climbing remains a challenging task due to their form. To execute ladder climbing motions effectively, a humanoid robot requires a reliable estimate of stability. Traditional methods such as Zero Moment Point are not applicable to vertical climbing, and do not account for force limits imposed on end-effectors. This dissertation implements a simple contact wrench space method using a linear combination of contact wrenches. Experiments in simulation showed ZMP equivalence on flat ground. Furthermore, the estimator was able to predict stability with four point contact on a vertical ladder. Finally, an extension of the presented method is proposed based on these findings to address the limitations of the linear combination.Ph.D., Mechanical Engineering and Mechanics -- Drexel University, 201

Design, modeling and implementation of a soft robotic neck for humanoid robots

Mención Internacional en el título de doctorSoft humanoid robotics is an emerging field that combines the flexibility and safety of soft robotics with the form and functionality of humanoid robotics. This thesis explores the potential for collaboration between these two fields with a focus on the development of soft joints for the humanoid robot TEO. The aim is to improve the robot’s adaptability and movement, which are essential for an efficient interaction with its environment. The research described in this thesis involves the development of a simple and easily transportable soft robotic neck for the robot, based on a 2 Degree of Freedom (DOF) Cable Driven Parallel Mechanism (CDPM). For its final integration into TEO, the proposed design is later refined, resulting in an efficiently scaled prototype able to face significant payloads. The nonlinear behaviour of the joints, due mainly to the elastic nature of their soft links, makes their modeling a challenging issue, which is addressed in this thesis from two perspectives: first, the direct and inverse kinematic models of the soft joints are analytically studied, based on CDPM mathematical models; second, a data-driven system identification is performed based on machine learning techniques. Both approaches are deeply studied and compared, both in simulation and experimentally. In addition to the soft neck, this thesis also addresses the design and prototyping of a soft arm capable of handling external loads. The proposed design is also tendon-driven and has a morphology with two main bending configurations, which provides more versatility compared to the soft neck. In summary, this work contributes to the growing field of soft humanoid robotics through the development of soft joints and their application to the humanoid robot TEO, showcasing the potential of soft robotics to improve the adaptability, flexibility, and safety of humanoid robots. The development of these soft joints is a significant achievement and the research presented in this thesis paves the way for further exploration and development in this field.La robótica humanoide blanda es un campo emergente que combina la flexibilidad y seguridad de la robótica blanda con la forma y funcionalidad de la robótica humanoide. Esta tesis explora el potencial de colaboración entre estos dos campos centrándose en el desarrollo de una articulación blanda para el cuello del robot humanoide TEO. El objetivo es mejorar la adaptabilidad y el movimiento del robot, esenciales para una interacción eficaz con su entorno. La investigación descrita en esta tesis consiste en el desarrollo de un prototipo sencillo y fácilmente transportable de cuello blando para el robot, basado en un mecanismo paralelo actuado por cable de 2 grados de libertad. Para su integración final en TEO, el diseño propuesto es posteriormente refinado, resultando en un prototipo eficientemente escalado capaz de manejar cargas significativas. El comportamiemto no lineal de estas articulaciones, debido fundamentalmente a la naturaleza elástica de sus eslabones blandos, hacen de su modelado un gran reto, que en esta tesis se aborda desde dos perspectivas diferentes: primero, los modelos cinemáticos directo e inverso de las articulaciones blandas se estudian analíticamente, basándose en modelos matemáticos de mecanismos paralelos actuados por cable; segundo, se aborda el problema de la identificación del sistema mediante técnicas basadas en machine learning. Ambas propuestas se estudian y comparan en profundidad, tanto en simulación como experimentalmente. Además del cuello blando, esta tesis también aborda el diseño de un brazo robótico blando capaz de manejar cargas externas. El diseño propuesto está igualmente basado en accionamiento por tendones y tiene una morfología con dos configuraciones principales de flexión, lo que proporciona una mayor versatilidad en comparación con el cuello robótico blando. En resumen, este trabajo contribuye al creciente campo de la robótica humanoide blanda mediante el desarrollo de articulaciones blandas y su aplicación al robot humanoide TEO, mostrando el potencial de la robótica blanda para mejorar la adaptabilidad, flexibilidad y seguridad de los robots humanoides. El desarrollo de estas articulaciones es una contribución significativa y la investigación presentada en esta tesis allana el camino hacia nuevos desarrollos y retos en este campo.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidenta: Cecilia Elisabet García Cena.- Secretario: Dorin Sabin Copaci.- Vocal: Martin Fodstad Stole

The Design and Realization of a Sensitive Walking Platform

Legged locomotion provides robots with the capability of adapting to different terrain conditions. General complex terrain traversal methodologies solely rely on proprioception which readily leads to instability under dynamical situations. Biological legged locomotion utilizes somatosensory feedback to sense the real-time interaction of the feet with ground to enhance stability. Nevertheless, limited attention has been given to sensing the feet-terrain interaction in robotics. This project introduces a paradigm shift in robotic walking called sensitive walking realized through the development of a compliant bipedal platform. Sensitive walking extends upon the success of sensitive manipulation which utilizes tactile feedback to localize an object to grasp, determine an appropriate manipulation configuration, and constantly adapts to maintain grasp stability. Based on the same concepts of sensitive manipulation, sensitive walking utilizes podotactile feedback to enhance real-time walking stability by effectively adapting to variations in the terrain. Adapting legged robotic platforms to sensitive walking is not as simple as attaching any tactile sensor to the feet of a robot. The sensors and the limbs need to have specific characteristics that support the implementation of the algorithms and allow the biped to safely come in contact with the terrain and detect the interaction forces. The challenges in handling the synergy of hardware and sensor design, and fabrication in a podotactile-based sensitive walking robot are addressed. The bipedal platform provides contact compliance through 12 series elastic actuators and contains 190 highly flexible tactile sensors capable of sensing forces at any incident angle. Sensitive walking algorithms are provided to handle multi-legged locomotion challenges including stairs and irregular terrain