A framework for compliant physical interaction : the grasp meets the task

Although the grasp-task interplay in our daily life is unquestionable, very little research has addressed this problem in robotics. In order to fill the gap between the grasp and the task, we adopt the most successful approaches to grasp and task specification, and extend them with additional elements that allow to define a grasp-task link. We propose a global sensor-based framework for the specification and robust control of physical interaction tasks, where the grasp and the task are jointly considered on the basis of the task frame formalism and the knowledge-based approach to grasping. A physical interaction task planner is also presented, based on the new concept of task-oriented hand pre-shapes. The planner focuses on manipulation of articulated parts in home environments, and is able to specify automatically all the elements of a physical interaction task required by the proposed framework. Finally, several applications are described, showing the versatility of the proposed approach, and its suitability for the fast implementation of robust physical interaction tasks in very different robotic systems

Robustness to external disturbances for legged robots using dynamic trajectory optimisation

In robotics, robustness is an important and desirable attribute of any system, from perception to planning and control. Robotic systems need to handle numerous factors of uncertainty when they are deployed, and the more robust a method is, the fewer chances there are of something going wrong. In planning and control, being robust is crucial to deal with uncertain contact timings and positions, mismatches in the dynamics model of the system, noise in the sensor readings and communication delays. In this thesis, we focus on the problem of dealing with uncertainty and external disturbances applied to the robot. Reactive robustness can be achieved at the control stage using a variety of control schemes. For example, model predictive control approaches are robust against external disturbances thanks to the online high-frequency replanning of the motion being executed. However, taking robustness into account in a proactive way, i.e., during the planning stage itself, enables the adoption of kinematic configurations that allow the system as a whole to better deal with uncertainty and disturbances. To this end, we propose a novel trajectory optimisation framework for robotic systems, ranging from fixed-base manipulators to legged robots, such as humanoids or quadrupeds equipped with arms. We tackle the problem from a first-principles perspective, and define a robustness metric based on the robot’s capabilities, such as the torques available to the system (considering actuator torque limits) and contact stability constraints. We compare our results with other existing approaches and, through simulation and experiments on the real robot, we show that our method is able to plan trajectories that are more robust against external disturbances

Proceedings of the NASA Conference on Space Telerobotics, volume 3

The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research

Stabilizer architecture for humanoid robots collaborating with humans

Hoy en día, los avances en las tecnologías de información y comunicación permiten el uso de robots como compañeros en las actividades con los seres humanos. Mientras que la mayoría de las investigaciones existentes se dedica a la interacción entre humanos y robots, el marco de esta investigación está centrado en el uso de robots como agentes de colaboración. En particular, este estudio está dedicado a los robots humanoides que puedan ayudar a la gente en varias tareas en entornos de trabajo. Los robots humanoides son sin duda los m as adecuados para este tipo de situaciones: pueden usar las mismas herramientas que los seres humanos y son lo m as probablemente aceptados por ellos. Después de explicar las ventajas de las tareas de colaboración entre los humanos y los robots y las diferencias con respecto a los sistemas de interacción y de teleoperación, este trabajo se centra en el nivel de las tecnologías que es necesario para lograr ese objetivo. El problema más complicado en el control de humanoides es el balance de la estructura. Este estudio se centra en técnicas novedosas para la estimación de la actitud del robot, que se utilizarán para el control. El control del robot se basa en un modelo muy conocido y simplificado: el péndulo invertido. Este modelo permite tener un control en tiempo real sobre la estructura, mientras que esté sometida a fuerzas externas / disturbios. Trayectorias suaves para el control de humanoides se han propuesto y probado en plataformas reales: éstos permiten reducir los impactos del robot con su entorno. Finalmente, el estudio extiende estos resultados a una contribución para la arquitectura de colaboración humano-humanoide. Dos tipos de colaboraciones humano humanoide se analizan: la colaboración física, donde robots y humanos comparten el mismo espacio y tienen un contacto físico (o por medio de un objeto), y una colaboración a distancia, en la que el ser humano está relativamente lejos del robot y los dos agentes colaboran por medio de una interfaz. El paradigma básico de esta colaboración robótica es: lo que es difícil (o peligroso) para el ser humano se hace por medio del robot y lo que es difícil para el robot lo puede mejor hacer el humano. Es importante destacar que el contexto de los experimentos no se basa en una unica plataforma humanoide; por el contrario, tres plataformas han sido objeto de los experimentos: se han empleado los robots HOAP-3, HRP-2 y TEO. ----------------------------------------------------------------------------------------------------------------------------------------------------------Nowadays, the advances in information and communication technologies permit the use of robots as companions in activities with humans. While most of the existing research is dedicated to the interaction between humans and robots, the framework of this research is the use of robots as collaborative agents. In particular, this study is dedicated to humanoid robots which should assist people in several tasks in working environments. Humanoid robots are certainly the most adequate for such situations: they can use the same tools as humans and are most likely accepted by them. After explaining the advantages of collaborative tasks among humans and robots and the differences with respect to interaction and teleoperation systems, this work focuses on the level of technologies which is necessary in order to achieve such a goal. The most complicated problem in humanoid control is the structure balance. This study focuses in novel techniques in the attitude estimation of the robot, to be used for the control. The control of the robot is based on a very well-known and simplified model: the double inverted pendulum. This model permits having a real-time control on the structure while submitted to external forces/disturbances. The control actions are strongly dependent on the three stability regions, which are determined by the position of the ZMP in the support polygon. Smooth trajectories for the humanoid control have been proposed and tested on real platforms: these permit reducing the impacts of the robot with its environment. Finally, the study extends these results to a contribution for human-humanoid collaboration architecture. Two types of human-humanoid collaborations are analyzed: a physical collaboration, where robot and human share the same space and have a physical contact (or by means of an object), and a remote collaboration, in which the human is relatively far away from the robot and the two agents collaborate using an interface. The basic paradigm for this robotic collaboration is: what is difficult (or dangerous) for the human is done by the robot and what is difficult for the robot is better done by the human. Importantly, the testing context is not based on a single humanoid platform; on the contrary, three platforms have been object of the experiments: the Hoap-3, HRP-2 and HRP2 robot have been employed

Cognitive Reasoning for Compliant Robot Manipulation

Physically compliant contact is a major element for many tasks in everyday environments. A universal service robot that is utilized to collect leaves in a park, polish a workpiece, or clean solar panels requires the cognition and manipulation capabilities to facilitate such compliant interaction. Evolution equipped humans with advanced mental abilities to envision physical contact situations and their resulting outcome, dexterous motor skills to perform the actions accordingly, as well as a sense of quality to rate the outcome of the task. In order to achieve human-like performance, a robot must provide the necessary methods to represent, plan, execute, and interpret compliant manipulation tasks. This dissertation covers those four steps of reasoning in the concept of intelligent physical compliance. The contributions advance the capabilities of service robots by combining artificial intelligence reasoning methods and control strategies for compliant manipulation. A classification of manipulation tasks is conducted to identify the central research questions of the addressed topic. Novel representations are derived to describe the properties of physical interaction. Special attention is given to wiping tasks which are predominant in everyday environments. It is investigated how symbolic task descriptions can be translated into meaningful robot commands. A particle distribution model is used to plan goal-oriented wiping actions and predict the quality according to the anticipated result. The planned tool motions are converted into the joint space of the humanoid robot Rollin' Justin to perform the tasks in the real world. In order to execute the motions in a physically compliant fashion, a hierarchical whole-body impedance controller is integrated into the framework. The controller is automatically parameterized with respect to the requirements of the particular task. Haptic feedback is utilized to infer contact and interpret the performance semantically. Finally, the robot is able to compensate for possible disturbances as it plans additional recovery motions while effectively closing the cognitive control loop. Among others, the developed concept is applied in an actual space robotics mission, in which an astronaut aboard the International Space Station (ISS) commands Rollin' Justin to maintain a Martian solar panel farm in a mock-up environment. This application demonstrates the far-reaching impact of the proposed approach and the associated opportunities that emerge with the availability of cognition-enabled service robots

Trajectory optimization for mobile manipulator motion planning

State-of-the-art robotics research has been progressively focusing on autonomous robots that can operate in unconstrained environments and interact with people. Specifically, manipulation tasks in Ambient Assisted Living environments are complex, involving an unknown number of parameters. Recent years show a trend of successfully applied machine learning approaches affecting day-to-day life. Similar tendencies are perceivable in robotics, existing methods being enhanced with learning-based components. This thesis studies approaches for incorporating task-specific knowledge into the motion planning process that can be shared across a heterogeneous fleet of robots. A step towards data-driven strategies will allow the field to break away from manuallytweaked, heuristics- or state-machine-based solutions and provide good scaling properties, while maintaining operation safety around humans at a very high level. The presented work proposes a motion planning framework employing Learning from Demonstration to encode task-specific motions, facilitating skill-transfer and improving state-of-the-art in motion planning. Resulting algorithms are compared against other methods in a series of everyday tasks. While different optimisation methods have different benefits, it is possible to build them into systems that both generalise and scale well with the number of tasks and number of robot platforms. This thesis shows that optimisation-based planners are ideal for incorporating prior knowledge into a motion-planning system

Proceedings of the NASA Conference on Space Telerobotics, volume 2

These proceedings contain papers presented at the NASA Conference on Space Telerobotics held in Pasadena, January 31 to February 2, 1989. The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research

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

Development of an assisted-teleoperation system for a dual-manipulator nuclear decommissioning robot

This thesis concerns a robotic platform that is being used for research into assisted tele–operation for common nuclear decommissioning tasks, such as remote handling and pipe cutting. The machine consists of dual, seven–function, hydraulically actuated HYDROLEK manipulators mounted (in prior research) on a mobile BROKK base unit. Whilst the original system was operated by remote control, the present thesis focusses on the development of a visual servoing system, in which the user selects the object of interest from an on–screen image, whilst the computer control system determines and implements via feedback control the required position and orientation of the manipulators. Novel research contributions are made in three main areas: (i) the development of a detailed mechanistic model of the system; (ii) the development and preliminary testing in the laboratory of the new assisted–teleoperation user interface; and (iii) the development of improved control systems for joint angle set point tracking, and their systematic, quantitative comparison via simulation and experiment. The mechanistic model builds on previous work, while the main novelty in this thesis relates to the hydraulic component of the model, and the development and evaluation of a multi–objective genetic algorithm framework to identify the unknown parameter values. To improve on the joystick direct teleoperation currently used as standard in the nuclear industry, which is slow and requires extensive operator training, the proposed assisted–teleoperation makes use of a camera mounted on the robot. Focussing on pipe cutting as an example, the new system ensures that one manipulator automatically grasps the user–selected pipe, and appropriately positions the second for a cutting operation. Initial laboratory testing (using a plastic pipe) shows the efficacy of the approach for positioning the manipulators, and suggests that for both experienced and inexperienced users, the task is completed significantly faster than via tele-operation. Finally, classical industrial, fuzzy logic, and novel state dependent parameter approaches to control are developed and compared, with the aim being to determine a relatively simple controller that yields good performance for the hydraulic manipulators. An improved, more structured method of dealing with the dead–zone characteristics is developed and implemented, replacing the rather ad hoc approach that had been utilised in previous research for the same machine

Compliance-based Affordance Templates for remote mobile manipulation

This thesis details the implementation of Affordance Templates with a compliance controller to pseudo-autonomously perform complex contact tasks in industrial environments. Multiple action planning methods were evaluated, and ATs were chosen as the best option for industry use due to their intuitive user interface. Two Affordance Template packages were implemented and evaluated to determine the package best suited for use by novice operators. That package was then implemented in conjunction with a compliance controller in the form of a remote demonstration to showcase reduction in operator cognitive burden by automatically managing contact forces and significant command latency (approx 250 ms) during task performance. The results showed that ATs greatly reduced task execution time and number of errors compared to manual control, with added compliance reducing AT set up time. Finally, this thesis introduces Affordance Primitives, which were created to increase the flexibility of Affordance Templates and present a path for future development in the field of automated task planning.Mechanical Engineerin