Supervised Autonomous Locomotion and Manipulation for Disaster Response with a Centaur-like Robot

Mobile manipulation tasks are one of the key challenges in the field of search and rescue (SAR) robotics requiring robots with flexible locomotion and manipulation abilities. Since the tasks are mostly unknown in advance, the robot has to adapt to a wide variety of terrains and workspaces during a mission. The centaur-like robot Centauro has a hybrid legged-wheeled base and an anthropomorphic upper body to carry out complex tasks in environments too dangerous for humans. Due to its high number of degrees of freedom, controlling the robot with direct teleoperation approaches is challenging and exhausting. Supervised autonomy approaches are promising to increase quality and speed of control while keeping the flexibility to solve unknown tasks. We developed a set of operator assistance functionalities with different levels of autonomy to control the robot for challenging locomotion and manipulation tasks. The integrated system was evaluated in disaster response scenarios and showed promising performance.Comment: In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 201

Methods to improve the coping capacities of whole-body controllers for humanoid robots

Current applications for humanoid robotics require autonomy in an environment specifically adapted to humans, and safe coexistence with people. Whole-body control is promising in this sense, having shown to successfully achieve locomotion and manipulation tasks. However, robustness remains an issue: whole-body controllers can still hardly cope with unexpected disturbances, with changes in working conditions, or with performing a variety of tasks, without human intervention. In this thesis, we explore how whole-body control approaches can be designed to address these issues. Based on whole-body control, contributions have been developed along three main axes: joint limit avoidance, automatic parameter tuning, and generalizing whole-body motions achieved by a controller. We first establish a whole-body torque-controller for the iCub, based on the stack-of-tasks approach and proposed feedback control laws in SE(3). From there, we develop a novel, theoretically guaranteed joint limit avoidance technique for torque-control, through a parametrization of the feasible joint space. This technique allows the robot to remain compliant, while resisting external perturbations that push joints closer to their limits, as demonstrated with experiments in simulation and with the real robot. Then, we focus on the issue of automatically tuning parameters of the controller, in order to improve its behavior across different situations. We show that our approach for learning task priorities, combining domain randomization and carefully selected fitness functions, allows the successful transfer of results between platforms subjected to different working conditions. Following these results, we then propose a controller which allows for generic, complex whole-body motions through real-time teleoperation. This approach is notably verified on the robot to follow generic movements of the teleoperator while in double support, as well as to follow the teleoperator\u2019s upper-body movements while walking with footsteps adapted from the teleoperator\u2019s footsteps. The approaches proposed in this thesis therefore improve the capability of whole-body controllers to cope with external disturbances, different working conditions and generic whole-body motions

Migration from Teleoperation to Autonomy via Modular Sensor and Mobility Bricks

In this thesis, the teleoperated communications of a Remotec ANDROS robot have been reverse engineered. This research has used the information acquired through the reverse engineering process to enhance the teleoperation and add intelligence to the initially automated robot. The main contribution of this thesis is the implementation of the mobility brick paradigm, which enables autonomous operations, using the commercial teleoperated ANDROS platform. The brick paradigm is a generalized architecture for a modular approach to robotics. This architecture and the contribution of this thesis are a paradigm shift from the proprietary commercial models that exist today. The modular system of sensor bricks integrates the transformed mobility platform and defines it as a mobility brick. In the wall following application implemented in this work, the mobile robotic system acquires intelligence using the range sensor brick. This application illustrates a way to alleviate the burden on the human operator and delegate certain tasks to the robot. Wall following is one among several examples of giving a degree of autonomy to an essentially teleoperated robot through the Sensor Brick System. Indeed once the proprietary robot has been altered into a mobility brick; the possibilities for autonomy are numerous and vary with different sensor bricks. The autonomous system implemented is not a fixed-application robot but rather a non-specific autonomy capable platform. Meanwhile the native controller and the computer-interfaced teleoperation are still available when necessary. Rather than trading off by switching from teleoperation to autonomy, this system provides the flexibility to switch between the two at the operator’s command. The contributions of this thesis reside in the reverse engineering of the original robot, its upgrade to a computer-interfaced teleoperated system, the mobility brick paradigm and the addition of autonomy capabilities. The application of a robot autonomously following a wall is subsequently implemented, tested and analyzed in this work. The analysis provides the programmer with information on controlling the robot and launching the autonomous function. The results are conclusive and open up the possibilities for a variety of autonomous applications for mobility platforms using modular sensor bricks

Sistema de aquisição de dados por interface háptica

Mestrado em Engenharia MecânicaNeste trabalho e apresentada uma interface háptica com realimentação de força para a teleoperação de um robô humanoide é que aborda um novo conceito destinado à aprendizagem por demonstração em robôs, denominado de ensino telecinestésico. A interface desenvolvida pretende promover o ensino cinestésico num ambiente de tele-robótica enriquecido pela virtualização háptica do ambiente e restrições do robô. Os dados recolhidos através desta poderão então ser usados em aprendizagem por demonstração, uma abordagem poderosa que permite aprender padrões de movimento sem a necessidade de modelos dinâmicos complexos, mas que geralmente é apresentada com demonstrações que não são fornecidas teleoperando os robôs. Várias experiências são referidas onde o ensino cinestésico em aprendizagem robótica foi utilizado com um sucesso considerável, bem como novas metodologias e aplicações com aparelhos hápticos. Este trabalho foi realizado com base na plataforma proprietária de 27 graus-de-liberdade do Projeto Humanoide da Universidade de Aveiro (PHUA), definindo novas methodologias de comando em tele-operação, uma nova abordagem de software e ainda algumas alterações ao hardware. Um simulador de corpo inteiro do robô em MATLAB SimMechanics é apresentado que é capaz de determinar os requisitos dinâmicos de binário de cada junta para uma dada postura ou movimento, exemplificando com um movimento efectuado para subir um degrau. Ir a mostrar algumas das potencialidades mas também algumas das limitações restritivas do software. Para testar esta nova abordagem tele-cinestésica são dados exemplos onde o utilizador pode desenvolver demonstrações interagindo fisicamente com o robô humanoide através de um joystick háptico PHANToM. Esta metodologia ir a mostrar que permite uma interação natural para o ensino e perceção tele-robóticos, onde o utilizador fornece instruções e correções funcionais estando ciente da dinâmica do sistema e das suas capacidades e limitações físicas. Ser a mostrado que a abordagem consegue atingir um bom desempenho mesmo com operadores inexperientes ou não familiarizados com o sistema. Durante a interação háptica, a informação sensorial e as ordens que guiam a uma tarefa específica podem ser gravados e posteriormente utilizados para efeitos de aprendizagem.In this work an haptic interface using force feedback for the teleoperation of a humanoid robot is presented, that approaches a new concept for robot learning by demonstration known as tele-kinesthethic teaching. This interface aims at promoting kinesthethic teaching in telerobotic environments enriched by the haptic virtualization of the robot's environment and restrictions. The data collected through this interface can later be in robot learning by demonstration, a powerful approach for learning motion patterns without complex dynamical models, but which is usually presented using demonstrations that are not provided by teleoperating the robots. Several experiments are referred where kinesthetic teaching for robot learning was used with considerable success, as well as other new methodologies and applications with haptic devices. This work was conducted on the proprietary 27 DOF University of Aveiro Humanoid Project (PHUA) robot, de ning new wiring and software solutions, as well as a new teleoperation command methodology. A MATLAB Sim- Mechanics full body robot simulator is presented that is able to determine dynamic joint torque requirements for a given robot movement or posture, exempli ed with a step climbing application. It will show some of the potentialities but also some restricting limitations of the software. To test this new tele-kinesthetic approach, examples are shown where the user can provide demonstrations by physically interacting with the humanoid robot through a PHANToM haptic joystick. This methodology will show that it enables a natural interface for telerobotic teaching and sensing, in which the user provides functional guidance and corrections while being aware of the dynamics of the system and its physical capabilities and / or constraints. It will also be shown that the approach can have a good performance even with inexperienced or unfamiliarized operators. During haptic interaction, the sensory information and the commands guiding the execution of a speci c task can be recorded and that data log from the human-robot interaction can be later used for learning purposes

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

Learning Algorithm Design for Human-Robot Skill Transfer

In this research, we develop an intelligent learning scheme for performing human-robot skills transfer. Techniques adopted in the scheme include the Dynamic Movement Prim- itive (DMP) method with Dynamic Time Warping (DTW), Gaussian Mixture Model (G- MM) with Gaussian Mixture Regression (GMR) and the Radical Basis Function Neural Networks (RBFNNs). A series of experiments are conducted on a Baxter robot, a NAO robot and a KUKA iiwa robot to verify the effectiveness of the proposed design.During the design of the intelligent learning scheme, an online tracking system is de- veloped to control the arm and head movement of the NAO robot using a Kinect sensor. The NAO robot is a humanoid robot with 5 degrees of freedom (DOF) for each arm. The joint motions of the operator’s head and arm are captured by a Kinect V2 sensor, and this information is then transferred into the workspace via the forward and inverse kinematics. In addition, to improve the tracking performance, a Kalman filter is further employed to fuse motion signals from the operator sensed by the Kinect V2 sensor and a pair of MYO armbands, so as to teleoperate the Baxter robot. In this regard, a new strategy is developed using the vector approach to accomplish a specific motion capture task. For instance, the arm motion of the operator is captured by a Kinect sensor and programmed through a processing software. Two MYO armbands with embedded inertial measurement units are worn by the operator to aid the robots in detecting and replicating the operator’s arm movements. For this purpose, the armbands help to recognize and calculate the precise velocity of motion of the operator’s arm. Additionally, a neural network based adaptive controller is designed and implemented on the Baxter robot to illustrate the validation forthe teleoperation of the Baxter robot.Subsequently, an enhanced teaching interface has been developed for the robot using DMP and GMR. Motion signals are collected from a human demonstrator via the Kinect v2 sensor, and the data is sent to a remote PC for teleoperating the Baxter robot. At this stage, the DMP is utilized to model and generalize the movements. In order to learn from multiple demonstrations, DTW is used for the preprocessing of the data recorded on the robot platform, and GMM is employed for the evaluation of DMP to generate multiple patterns after the completion of the teaching process. Next, we apply the GMR algorithm to generate a synthesized trajectory to minimize position errors in the three dimensional (3D) space. This approach has been tested by performing tasks on a KUKA iiwa and a Baxter robot, respectively.Finally, an optimized DMP is added to the teaching interface. A character recombination technology based on DMP segmentation that uses verbal command has also been developed and incorporated in a Baxter robot platform. To imitate the recorded motion signals produced by the demonstrator, the operator trains the Baxter robot by physically guiding it to complete the given task. This is repeated five times, and the generated training data set is utilized via the playback system. Subsequently, the DTW is employed to preprocess the experimental data. For modelling and overall movement control, DMP is chosen. The GMM is used to generate multiple patterns after implementing the teaching process. Next, we employ the GMR algorithm to reduce position errors in the 3D space after a synthesized trajectory has been generated. The Baxter robot, remotely controlled by the user datagram protocol (UDP) in a PC, records and reproduces every trajectory. Additionally, Dragon Natural Speaking software is adopted to transcribe the voice data. This proposed approach has been verified by enabling the Baxter robot to perform a writing task of drawing robot has been taught to write only one character

Exploring Natural User Abstractions For Shared Perceptual Manipulator Task Modeling & Recovery

State-of-the-art domestic robot assistants are essentially autonomous mobile manipulators capable of exerting human-scale precision grasps. To maximize utility and economy, non-technical end-users would need to be nearly as efficient as trained roboticists in control and collaboration of manipulation task behaviors. However, it remains a significant challenge given that many WIMP-style tools require superficial proficiency in robotics, 3D graphics, and computer science for rapid task modeling and recovery. But research on robot-centric collaboration has garnered momentum in recent years; robots are now planning in partially observable environments that maintain geometries and semantic maps, presenting opportunities for non-experts to cooperatively control task behavior with autonomous-planning agents exploiting the knowledge. However, as autonomous systems are not immune to errors under perceptual difficulty, a human-in-the-loop is needed to bias autonomous-planning towards recovery conditions that resume the task and avoid similar errors. In this work, we explore interactive techniques allowing non-technical users to model task behaviors and perceive cooperatively with a service robot under robot-centric collaboration. We evaluate stylus and touch modalities that users can intuitively and effectively convey natural abstractions of high-level tasks, semantic revisions, and geometries about the world. Experiments are conducted with \u27pick-and-place\u27 tasks in an ideal \u27Blocks World\u27 environment using a Kinova JACO six degree-of-freedom manipulator. Possibilities for the architecture and interface are demonstrated with the following features; (1) Semantic \u27Object\u27 and \u27Location\u27 grounding that describe function and ambiguous geometries (2) Task specification with an unordered list of goal predicates, and (3) Guiding task recovery with implied scene geometries and trajectory via symmetry cues and configuration space abstraction. Empirical results from four user studies show our interface was much preferred than the control condition, demonstrating high learnability and ease-of-use that enable our non-technical participants to model complex tasks, provide effective recovery assistance, and teleoperative control

Homecare Robotic Systems for Healthcare 4.0: Visions and Enabling Technologies

Powered by the technologies that have originated from manufacturing, the fourth revolution of healthcare technologies is happening (Healthcare 4.0). As an example of such revolution, new generation homecare robotic systems (HRS) based on the cyber-physical systems (CPS) with higher speed and more intelligent execution are emerging. In this article, the new visions and features of the CPS-based HRS are proposed. The latest progress in related enabling technologies is reviewed, including artificial intelligence, sensing fundamentals, materials and machines, cloud computing and communication, as well as motion capture and mapping. Finally, the future perspectives of the CPS-based HRS and the technical challenges faced in each technical area are discussed