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Humans can react extremely rapidly in the face of unexpected changes in the environment. This is best illustrated in sports, when tennis players run and return a fast ball flying at a speed of around 73 m=s. Robots, on the other hand, remain slow and clumsy to adapt to perturbations, despite the fact that computers process information orders of magnitude faster than the human brain. This thesis targets the design of controllers to endow robot with extremely fast and appropriate reactivity in the face of unforeseen changes in the environment. As a benchmark, we chose the challenging task of catching objects in flight. To react appropriately to perturbations requires the ability to detect and predict the effect of the observed changes in the environment and to adapt its control plan adequately. This thesis, hence, addresses first the problem of predicting accurately the flying trajectory of the object. We propose a model-free method to estimate the dynamics of free-flying objects. We take a realistic perspective to the problem and investigate tracking accurately and very rapidly the trajectory and orientation of an object so as to catch it in flight. We consider the dynamics of complex objects where the grasping point is not located at the center of mass, without having any prior information on the physical properties of the object. We also consider the dynamics of non-rigid object (such as a half-filled bottle). It is challenging as inertial properties of the object are not even constant and may change during flight. To achieve this, a density estimate of the translational and rotational acceleration is built based on the trajectories of various examples by using a machine learning approach. The estimated model of the objectís dynamics is a closed form solution, and it is used in conjunction with an Extended Kalman Filter for robust tracking in the face of noisy sensing. We validate the approach for real-time motion tracking of 5 daily life objects with complex dynamics (a ball, a fully-filled bottle, a half-filled bottle, a hammer and a pingpong racket). In the second part of the thesis, we propose a novel methodology for learning when and where to grasp the flying object. We develop a data-driven probabilistic approach to estimate a distribution of admissible grasping postures on the object and to compute the robotís reachable space. The robot is thus able to determine the feasible catching arm configuration (mid-flight intercept point) along the predicted object trajectory in real-time. The method is extended first to compute feasible bimanual grasping posture, in a second stage, to determine whole-body catching posture of a 29 degrees of freedom (DOF) humanoid robot (excluding the fingers and neck). Finally, we show that the models of reachable space which we developed for our robotic framework can explain observed preference in posture in human catching motions. In the last part of the thesis, we address the issue of adapting on the fly the robotís arm motion so as to catch the flying object on time. We adopt a dynamical system (DS) approach to control simultaneously and in coordination the motion of the arm and fingers, so that the fingers close on time on the object. Additionally, we propose a system to synchronize the robotís motion with that of the fast moving objects, while benefitting from all the robustness properties deriving from the DS. Furthermore, we propose a generalized human-like inversekinematics solution, by modeling human-like characteristics (the degree of torso orientation and elbow elevation) from human demonstrations and by applying the model to a generalized inverse kinematic problem. The humanoid robot is thus able to increase the human-likeness while it executes the trained task-space motion. We have validated the methods developed in the thesis, in simulation and real-world experiment with different robot platforms, iCub (53 DOF) and COMAN (29 DOF) humanoid robots and KUKA LWR robot arm (7 DOF). In particular, we demonstrated the extremely fast speed of our method in an impressive demonstration, whereby the KUKA LWR robot arm catches in-flight different objects with uneven mass distribution, such as a tennis racket and a bottle partly filled with water. We believe that our methods significantly advances the field, in offering an example of ultra-fast control in the face of uncertainty.


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Last update: 25/08/06