Learning Robust Task Priorities of QP-Based Whole-Body Torque-Controllers

Generating complex whole-body movements for humanoid robots is now most often achieved with multi-task whole-body controllers based on quadratic programming. To perform on the real robot, such controllers often require a human expert to tune or optimize the many parameters of the controller related to the tasks and to the specific robot, which is generally reported as a tedious and time consuming procedure. This problem can be tackled by automatically optimizing some parameters such as task priorities or task trajectories, while ensuring constraints satisfaction, through simulation. However, this does not guarantee that parameters optimized in simulation will also be optimal for the real robot. As a solution, the present paper focuses on optimizing task priorities in a robust way, by looking for solutions which achieve desired tasks under a variety of conditions and perturbations. This approach, which can be referred to as domain randomization, can greatly facilitate the transfer of optimized solutions from simulation to a real robot. The proposed method is demonstrated using a simulation of the humanoid robot iCub for a whole-body stepping task

Learning robust task priorities of QP-based whole-body torque-controllers

International audienceGenerating complex whole-body movements for humanoid robots is now most often achieved with multi-task whole-body controllers based on quadratic programming. To perform on the real robot, such controllers often require a human expert to tune or optimize the many parameters of the controller related to the tasks and to the specific robot, which is generally reported as a tedious and time consuming procedure. This problem can be tackled by automatically optimizing some parameters such as task priorities or task trajectories, while ensuring constraints satisfaction, through simulation. However, this does not guarantee that parameters optimized in simulation will also be optimal for the real robot. As a solution, the present paper focuses on optimizing task priorities in a robust way, by looking for solutions which achieve desired tasks under a variety of conditions and perturbations. This approach, which can be referred to as domain randomization, can greatly facilitate the transfer of optimized solutions from simulation to a real robot. The proposed method is demonstrated using a simulation of the humanoid robot iCub for a whole-body stepping task

Instantaneous Momentum-Based Control of Floating Base Systems

In the last two decades a growing number of robotic applications such as autonomous drones, wheeled robots and industrial manipulators started to be employed in several human environments. However, these machines often possess limited locomotion and/or manipulation capabilities, thus reducing the number of achievable tasks and increasing the complexity of robot-environment interaction. Augmenting robots locomotion and manipulation abilities is a fundamental research topic, with a view to enhance robots participation in complex tasks involving safe interaction and cooperation with humans. To this purpose, humanoid robots, aerial manipulators and the novel design of flying humanoid robots are among the most promising platforms researchers are studying in the attempt to remove the existing technological barriers. These robots are often modeled as floating base systems, and have lost the assumption -- typical of fixed base robots -- of having one link always attached to the ground. From the robot control side, contact forces regulation revealed to be fundamental for the execution of interaction tasks. Contact forces can be influenced by directly controlling the robot's momentum rate of change, and this fact gives rise to several momentum-based control strategies. Nevertheless, effective design of force and torque controllers still remains a complex challenge. The variability of sensor load during interaction, the inaccuracy of the force/torque sensing technology and the inherent nonlinearities of robot models are only a few complexities impairing efficient robot force control. This research project focuses on the design of balancing and flight controllers for floating base robots interacting with the surrounding environment. More specifically, the research is built upon the state-of-the-art of momentum-based controllers and applied to three robotic platforms: the humanoid robot iCub, the aerial manipulator OTHex and the jet-powered humanoid robot iRonCub. The project enforces the existing literature with both theoretical and experimental results, aimed at achieving high robot performances and improved stability and robustness, in presence of different physical robot-environment interactions

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

Télé-opération Corps Complet de Robots Humanoïdes

This thesis aims to investigate systems and tools for teleoperating a humanoid robot. Robotteleoperation is crucial to send and control robots in environments that are dangerous or inaccessiblefor humans (e.g., disaster response scenarios, contaminated environments, or extraterrestrialsites). The term teleoperation most commonly refers to direct and continuous control of a robot.In this case, the human operator guides the motion of the robot with her/his own physical motionor through some physical input device. One of the main challenges is to control the robot in a waythat 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 remotesensors in order to comprehend the situation or feel physically present at the site, producingeffective robot behaviors. Complications arise when the communication network is non-ideal. Inthis case the commands from human to robot together with the feedback from robot to human canbe delayed. These delays can be very disturbing for the human operator, who cannot teleoperatetheir robot avatar in an effective way.Another crucial point to consider when setting up a teleoperation system is the large numberof parameters that have to be tuned to effectively control the teleoperated robots. Machinelearning approaches and stochastic optimizers can be used to automate the learning of some of theparameters.In this thesis, we proposed a teleoperation system that has been tested on the humanoid robotiCub. We used an inertial-technology-based motion capture suit as input device to control thehumanoid 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 motionretargeting approach that achieves human-likeness while trying to ensure the feasibility of thetransferred motion. We then implemented a whole-body controller to enable the robot to trackthe retargeted human motion. The controller has been later optimized in simulation to achieve agood tracking of the whole-body reference movements, by recurring to a multi-objective stochasticoptimizer, which allowed us to find robust solutions working on the real robot in few trials.To teleoperate walking motions, we implemented a higher-level teleoperation mode in whichthe user can use a joystick to send reference commands to the robot. We integrated this setting inthe teleoperation system, which allows the user to switch between the two different modes.A major problem preventing the deployment of such systems in real applications is the presenceof communication delays between the human input and the feedback from the robot: evena 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 executescommands before it actually receives them, so that the visual feedback appears to be synchronizedto the operator, whereas the robot executed the commands in the past. To do so, the robot continuouslypredicts future commands by querying a machine learning model that is trained on pasttrajectories and conditioned on the last received commands.Cette thèse vise à étudier des systèmes et des outils pour la télé-opération d’un robot humanoïde.La téléopération de robots est cruciale pour envoyer et contrôler les robots dans des environnementsdangereux ou inaccessibles pour les humains (par exemple, des scénarios d’interventionen cas de catastrophe, des environnements contaminés ou des sites extraterrestres). Le terme téléopérationdésigne le plus souvent le contrôle direct et continu d’un robot. Dans ce cas, l’opérateurhumain guide le mouvement du robot avec son propre mouvement physique ou via un dispositifde contrôle. L’un des principaux défis est de contrôler le robot de manière à garantir son équilibredynamique tout en essayant de suivre les références humaines. De plus, l’opérateur humain abesoin d’un retour d’information sur l’état du robot et de son site via des capteurs à distance afind’appréhender la situation ou de se sentir physiquement présent sur le site, produisant des comportementsde robot efficaces. Des complications surviennent lorsque le réseau de communicationn’est pas idéal. Dans ce cas, les commandes de l’homme au robot ainsi que la rétroaction du robotà l’homme peuvent être retardées. Ces délais peuvent être très gênants pour l’opérateur humain,qui ne peut pas télé-opérer efficacement son avatar robotique.Un autre point crucial à considérer lors de la mise en place d’un système de télé-opérationest le grand nombre de paramètres qui doivent être réglés pour contrôler efficacement les robotstélé-opérés. Des approches d’apprentissage automatique et des optimiseurs stochastiques peuventêtre utilisés pour automatiser l’apprentissage de certains paramètres.Dans cette thèse, nous avons proposé un système de télé-opération qui a été testé sur le robothumanoïde iCub. Nous avons utilisé une combinaison de capture de mouvement basée sur latechnologie inertielle comme périphérique de contrôle pour l’humanoïde et un casque de réalitévirtuelle connecté aux caméras du robot pour obtenir un retour visuel. Nous avons d’abord traduitles mouvements humains en mouvements robotiques équivalents en développant une approchede retargeting de mouvement qui atteint la ressemblance humaine tout en essayant d’assurer lafaisabilité du mouvement transféré. Nous avons ensuite implémenté un contrôleur du corps entierpour permettre au robot de suivre le mouvement humain reciblé. Le contrôleur a ensuite étéoptimisé en simulation pour obtenir un bon suivi des mouvements de référence du corps entier,en recourant à un optimiseur stochastique multi-objectifs, ce qui nous a permis de trouver dessolutions robustes fonctionnant sur le robot réel en quelques essais.Pour télé-opérer les mouvements de marche, nous avons implémenté un mode de télé-opérationde niveau supérieur dans lequel l’utilisateur peut utiliser un joystick pour envoyer des commandesde référence au robot. Nous avons intégré ce paramètre dans le système de télé-opération, ce quipermet à l’utilisateur de basculer entre les deux modes différents.Un problème majeur empêchant le déploiement de tels systèmes dans des applications réellesest la présence de retards de communication entre l’entrée humaine et le retour du robot: mêmequelques centaines de millisecondes de retard peuvent irrémédiablement perturber l’opérateur,encore plus quelques secondes. Pour surmonter ces retards, nous avons introduit un système danslequel un robot humanoïde exécute des commandes avant de les recevoir, de sorte que le retourvisuel semble être synchronisé avec l’opérateur, alors que le robot exécutait les commandes dansle passé. Pour ce faire, le robot prédit en permanence les commandes futures en interrogeant unmodèle d’apprentissage automatique formé sur les trajectoires passées et conditionné aux dernièrescommandes reçues