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UID:16210-1654596000-1654603200@www.loria.fr
SUMMARY:PhD defense of Luigi Penco (Larsen)
DESCRIPTION:Luigi Penco will defend his thesis on June 7\, 2022 at 10 am\, in Room A008. His presentation is entitled: « Whole-body teleoperation of humanoid robots ».\n\nAbstract:\nThis thesis aims to investigate systems and tools for teleoperating a humanoid robot. Robot teleoperation is crucial to send and control robots in environments that are dangerous or inaccessible for humans (e.g.\, disaster response scenarios\, contaminated environments\, or extraterrestrial sites). The term teleoperation most commonly refers to direct and continuous remote control of a robot. In this case\, the human operator guides the motion of the robot with her/his own physical motion or through some physical input device. One of the main challenges is to control the robot in a way that guarantees its dynamical balance while trying to follow the human references. In addition\, the human operator needs some feedback about the state of the robot and its work site through remote sensors in order to comprehend the situation or feel physically present at the site\, producing effective robot behaviors. Complications arise when the communication network is non-ideal. In this case the commands from human to robot together with the feedback from robot to human can be delayed. These delays can be very disturbing for the human operator\, who cannot teleoperate their robot avatar in an effective way. \nAnother crucial point to consider when setting up a teleoperation system is the large number of parameters that have to be tuned to effectively control the teleoperated robots. Machine learning approaches and stochastic optimizers can be used to automate the learning of some of the parameters. \nIn this thesis\, we proposed a teleoperation system that has been tested on the humanoid robot iCub. We used an inertial-technology-based motion capture suit as input device to control the humanoid and a virtual reality headset connected to the robot cameras to get some visual feedback. We first translated the human movements into equivalent robot ones by developping a motion retargeting approach that achieves human-likeness while trying to ensure the feasibility of the transferred motion. We then implemented a whole-body controller to enable the robot to track the retargeted human motion. The controller has been later optimized in simulation to achieve a good tracking of the whole-body reference movements\, by recurring to a multi-objective stochastic optimizer\, which allowed us to find robust solutions working on the real robot in few trials. \nTo teleoperate walking motions\, we implemented a higher-level teleoperation mode in which the user can use a joystick to send reference commands to the robot. We integrated this setting in the teleoperation system\, which allows the user to switch between the two different modes. \nA major problem preventing the deployment of such systems in real applications is the presence of communication delays between the human input and the feedback from the robot: even a few hundred milliseconds of delay can irremediably disturb the operator\, let alone a few seconds. To overcome these delays\, we introduced a system in which a humanoid robot executes commands before it actually receives them\, so that the visual feedback appears to be synchronized to the operator\, whereas the robot executed the commands in the past. To do so\, the robot continuously predicts future commands by querying a machine learning model that is trained on past trajectories and conditioned on the last received commands. \n\n\n\n\nJury members :\nSupervisors:\n\nDr. Serena Ivaldi\, Inria Nancy Grand-Est\nDr. Jean-Baptiste Mouret\, Inria Nancy Grand-Est\n\n\nReviewers (remote):\n\nProf. Dr. Dongheui Lee (이동희)\, TU Wien\nDr. Olivier Stasse\, LAAS Toulouse\n\n\nExaminers (remote):\n\nDr. Paolo Robuffo Giordano\, Inria/IRISA Rennes\nDr. Jerry Pratt\, Florida Institute for Human & Machine Cognition
URL:https://www.loria.fr/event/phd-defense-of-luigi-penco-larsen/
LOCATION:A008
CATEGORIES:Soutenance
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DTSTART;TZID=Europe/Paris:20220627T133000
DTEND;TZID=Europe/Paris:20220627T150000
DTSTAMP:20220624T073704Z
CREATED:20220624T073704Z
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UID:16337-1656336600-1656342000@www.loria.fr
SUMMARY:PhD defense: Vladislav Tempez (Larsen)
DESCRIPTION:Vladislav Tempez will defend his thesis on Monday\, 27th June at 1.30pm in room C005. \nHis presentation will be in French and is entitled « Apprentissage d’une loi de commande optimale d’un petit quadrotor pour le vol dans des tuyaux cylindriques ». \nRésumé: \n« Cette thèse traite du vol de petits quadrotors (en particulier du modèle Crazyflie\, ∼ 10cm\, 30g) dans des environnements confinés comme les tuyaux. Trois problématiques sont principalement abordées dans cette thèse : la présence et la nature de perturbations dues à l’interaction entre les flux d’air déplacés par les rotors et les parois de l’environnement\, la conception d’un contrôleur pour le vol dans un tel environnement malgré les perturbations et l’adaptation de ce contrôleur\naux contraintes de fonctionnement à bord d’un petit quadrotor comme le Crazyflie.\nLes perturbations aérodynamiques sont mesurées en régime statique et nous présentons une cartographie de celles-ci en un ensemble de points donné de l’environnement. Ces mesures sont utilisées pour dériver un modèle capable de réaliser des prédictions de ces perturbations en tout point de l’environnement.\nLe contrôleur proposé pour voler dans un tel environnement est un contrôleur MPC basé sur la résolution de problèmes de contrôle optimal intégrant le modèle des perturbations\, permettant ainsi à la fois la planification de la navigation en tenant compte des perturbations à venir et le rejet de celles qui seraient hors modèle.\nLa résolution de ces problèmes de contrôle optimal étant trop coûteuse en calcul pour être réalisée en temps réel à bord d’un petit quadrotor comme le Crazyflie\, cette thèse aborde ensuite l’apprentissage par imitation du contrôleur MPC par un réseau de neurones qui permet d’obtenir une approximation de ce contrôleur dont le coût en calcul est compatible avec l’utilisation à bord d’un Crazyflie. » \nAbstract: \n« This work deals with the flight of small quadrotors (essentially the Crazyflie\, ∼10cm\, 30g) for indoor environments such as pipes. Three main questions are dealt with in the thesis : the existence and nature of aerodynamic perturbations caused by the interaction of rotors’ airflow with the environment\, the design of a flight controller fit for environments like pipes and the adaptation of this controller to be embedded onboard a Crazyflie controller. The static component of aerodynamic perturbations is measured and we detail a map of these\nfor a given set of locations in the environment. These measures are then used to build a model that is able to predict these perturbations at any point of the environment.\nWe propose a MPC flight controller based on the resolution of the optimal control problem\, taking the perturbation model into account. This controller is able to plan trajectories considering the perturbations and to reject these perturbations.\nSolving the optimal control problem being too computationally expensive to be done in real time onboard the Crazyflie\, we propose to learn a neural network approximating the MPC flight\ncontroller in a supervised way\, by imitation. This neural network approximation can be executed in real time onboard a Crazyflie. »
URL:https://www.loria.fr/event/phd-defense-vladislav-tempez-larsen/
LOCATION:C005
CATEGORIES:Soutenance
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