1,056 research outputs found
Robot eye-hand coordination learning by watching human demonstrations: a task function approximation approach
We present a robot eye-hand coordination learning method that can directly
learn visual task specification by watching human demonstrations. Task
specification is represented as a task function, which is learned using inverse
reinforcement learning(IRL) by inferring differential rewards between state
changes. The learned task function is then used as continuous feedbacks in an
uncalibrated visual servoing(UVS) controller designed for the execution phase.
Our proposed method can directly learn from raw videos, which removes the need
for hand-engineered task specification. It can also provide task
interpretability by directly approximating the task function. Besides,
benefiting from the use of a traditional UVS controller, our training process
is efficient and the learned policy is independent from a particular robot
platform. Various experiments were designed to show that, for a certain DOF
task, our method can adapt to task/environment variances in target positions,
backgrounds, illuminations, and occlusions without prior retraining.Comment: Accepted in ICRA 201
Visual guidance of unmanned aerial manipulators
The ability to fly has greatly expanded the possibilities for robots to perform surveillance, inspection or map generation tasks. Yet it was only in recent years that research in aerial robotics was mature enough to allow active interactions with the environment. The robots responsible for these interactions are called aerial manipulators and usually combine a multirotor platform and one or more robotic arms.
The main objective of this thesis is to formalize the concept of aerial manipulator and present guidance methods, using visual information, to provide them with autonomous functionalities.
A key competence to control an aerial manipulator is the ability to localize it in the environment.
Traditionally, this localization has required external infrastructure of sensors (e.g., GPS or IR cameras), restricting the real applications. Furthermore, localization methods with on-board sensors, exported from other robotics fields such as simultaneous localization and mapping (SLAM), require large computational units becoming a handicap in vehicles where size, load,
and power consumption are important restrictions. In this regard, this thesis proposes a method to estimate the state of the vehicle (i.e., position, orientation, velocity and acceleration) by means of on-board, low-cost, light-weight and high-rate sensors.
With the physical complexity of these robots, it is required to use advanced control techniques during navigation. Thanks to their redundancy on degrees-of-freedom, they offer the possibility to accomplish not only with mobility requirements but with other tasks simultaneously and hierarchically, prioritizing them depending on their impact to the overall mission success. In this work we present such control laws and define a number of these tasks to drive the vehicle using visual information, guarantee the robot integrity during flight, and improve
the platform stability or increase arm operability.
The main contributions of this research work are threefold: (1) Present a localization technique to allow autonomous navigation, this method is specifically designed for aerial platforms with size, load and computational burden restrictions. (2) Obtain control commands to drive the vehicle using visual information (visual servo). (3) Integrate the visual servo commands into
a hierarchical control law by exploiting the redundancy of the robot to accomplish secondary tasks during flight. These tasks are specific for aerial manipulators and they are also provided.
All the techniques presented in this document have been validated throughout extensive experimentation with real robotic platforms.La capacitat de volar ha incrementat molt les possibilitats dels robots per a realitzar tasques de vigilà ncia, inspecció o generació de mapes. Tot i això, no és fins fa pocs anys que la recerca en robòtica aèria ha estat prou madura com per començar a permetre interaccions amb l’entorn d’una manera activa. Els robots per a fer-ho s’anomenen manipuladors aeris i habitualment combinen una plataforma multirotor i un braç robòtic.
L’objectiu d’aquesta tesi és formalitzar el concepte de manipulador aeri i presentar mètodes de guiatge, utilitzant informació visual, per dotar d’autonomia aquest tipus de vehicles.
Una competència clau per controlar un manipulador aeri Ă©s la capacitat de localitzar-se en l’entorn. Tradicionalment aquesta localitzaciĂł ha requerit d’infraestructura sensorial externa (GPS, cĂ meres IR, etc.), limitant aixĂ les aplicacions reals. Pel contrari, sistemes de localitzaciĂł exportats d’altres camps de la robòtica basats en sensors a bord, com per exemple mètodes de localitzaciĂł i mapejat simultĂ nis (SLAM), requereixen de gran capacitat de còmput, caracterĂstica que penalitza molt en vehicles on la mida, pes i consum elèctric son grans restriccions. En aquest sentit, aquesta tesi proposa un mètode d’estimaciĂł d’estat del robot (posiciĂł, velocitat, orientaciĂł i acceleraciĂł) a partir de sensors instal·lats a bord, de baix cost, baix consum computacional i que proporcionen mesures a alta freqüència.
Degut a la complexitat fĂsica d’aquests robots, Ă©s necessari l’ús de tècniques de control avançades. GrĂ cies a la seva redundĂ ncia de graus de llibertat, aquests robots ens ofereixen la possibilitat de complir amb els requeriments de mobilitat i, simultĂ niament, realitzar tasques de manera jerĂ rquica, ordenant-les segons l’impacte en l’acompliment de la missiĂł. En aquest treball es presenten aquestes lleis de control, juntament amb la descripciĂł de tasques per tal de guiar visualment el vehicle, garantir la integritat del robot durant el vol, millorar de l’estabilitat del vehicle o augmentar la manipulabilitat del braç.
Aquesta tesi es centra en tres aspectes fonamentals: (1) Presentar una tècnica de localitzaciĂł per dotar d’autonomia el robot. Aquest mètode estĂ especialment dissenyat per a plataformes amb restriccions de capacitat computacional, mida i pes. (2) Obtenir les comandes de control necessĂ ries per guiar el vehicle a partir d’informaciĂł visual. (3) Integrar aquestes accions dins una estructura de control jerĂ rquica utilitzant la redundĂ ncia del robot per complir altres tasques durant el vol. Aquestes tasques son especĂfiques per a manipuladors aeris i tambĂ© es defineixen en aquest document.
Totes les tècniques presentades en aquesta tesi han estat avaluades de manera experimental amb plataformes robòtiques real
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Information acquisition using eye-gaze tracking for person-following with mobile robots
In the effort of developing natural means for human-robot interaction (HRI), signifcant amount of research has been focusing on Person-Following (PF) for mobile robots. PF, which generally consists of detecting, recognizing and following people, is believed to be one of the required functionalities for most future robots that share their environments with their human companions. Research in this field is mostly directed towards fully automating this functionality, which makes the challenge even more tedious. Focusing on this challenge leads research to divert from other challenges that coexist in any PF system. A natural PF functionality consists of a number of tasks that are required to be implemented in the system. However, in more realistic life scenarios, not all the tasks required for PF need to be automated. Instead, some of these tasks can be operated by human operators and therefore require natural means of interaction and information acquisition. In order to highlight all the tasks that are believed to exist in any PF system, this paper introduces a novel taxonomy for PF. Also, in order to provide a natural means for HRI, TeleGaze is used for information acquisition in the implementation of the taxonomy. TeleGaze was previously developed by the authors as a means of natural HRI for teleoperation through eye-gaze tracking. Using TeleGaze in the aid of developing PF systems is believed to show the feasibility of achieving a realistic information acquisition in a natural way
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