Rapid and reactive robot control framework for catching objects in flight

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@PhdThesis{Kim2014-ID904,
  author =       "Seungsu Kim",
  title =        "Rapid and reactive robot control framework for
                 catching objects in flight",
  school =       "Ecole Polytechnique Federale de Lausanne",
  year =         "2014",
  address =      "Switzerland",
  month =        "13 " # feb,
  keywords =     "genetic algorithms, genetic programming, robot
                 catching, imitation learning, reachable-space,
                 graspable space",
  URL =          "https://www.youtube.com/watch?v=24TCUZISIdU",
  URL =          "http://lasa.epfl.ch/publications/bibtex.php?ID=904",
  URL =          "http://lasa.epfl.ch/publications/uploadedFiles/EPFL_TH6094.pdf",
  size =         "134 pages",
  abstract =     "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
                 meters per second. 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 centre 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 object's
                 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 ping pong
                 racket).",
  abstract =     "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
                 robot's 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 bi-manual 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 robot's 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 synchronise the robot's motion
                 with that of the fast moving objects, while benefiting
                 from all the robustness properties deriving from the
                 DS. Furthermore, we propose a generalised human-like
                 inverse kinematics solution, by modelling human-like
                 characteristics (the degree of torso orientation and
                 elbow elevation) from human demonstrations and by
                 applying the model to a generalised 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.",
  notes =        "In English. Supervisor Prof. Aude Billard, directrice
                 de these.

                 Suisse Thesis Number 6094 (2014) EPFL Docteur es
                 sciences. GPTIPS",
}

Genetic Programming entries for Seungsu Kim

Citations