morante2014action.bib

@INPROCEEDINGS{6907098,
author={S. Morante and J. G. Victores and A. Jardón and C. Balaguer},
booktitle={2014 IEEE International Conference on Robotics and Automation (ICRA)},
title={Action effect generalization, recognition and execution through Continuous Goal-Directed Actions},
year={2014},
pages={1822-1827},
abstract={Programming by demonstration (PbD) allows matching the kinematic movements of a robot with those of a human. The presented Continuous Goal-Directed Actions (CGDA) is able to additionally encode the effects of a demonstrated action, which are not encoded in PbD. CGDA allows generalization, recognition and execution of action effects on the environment. In addition to analyzing kinematic parameters (joint positions/velocities, etc.), CGDA focuses on changes produced on the object due to an action (spatial, color, shape, etc.). By tracking object features during action execution, we create a trajectory in an n-dimensional feature space that represents object temporal states. Discretized action repetitions provide us with a cloud of points. Action generalization is accomplished by extracting the average point of each sequential temporal interval of the point cloud. These points are interpolated using Radial Basis Functions, obtaining a generalized multidimensional object feature trajectory. Action recognition is performed by comparing the trajectory of a query sample with the generalizations. The trajectories discrepancy score is obtained by using Dynamic Time Warping (DTW). Robot joint trajectories for execution are computed in a simulator through evolutionary computation. Object features are extracted from sensors, and each evolutionary individual fitness is measured using DTW, comparing the simulated action with the generalization.},
keywords={automatic programming;generalisation (artificial intelligence);image recognition;learning (artificial intelligence);radial basis function networks;robot kinematics;robot programming;robot vision;action effect execution;action effect generalization;action effect recognition;continuous goal-directed actions;discretized action repetitions;dynamic time warping;evolutionary computation;evolutionary individual fitness;generalized multidimensional object feature trajectory;interpolation;n-dimensional feature space;object feature extraction;object feature tracking;object temporal states;point cloud;programming by demonstration;radial basis functions;robot joint trajectories;robot kinematic movements;sequential temporal interval;Color;Feature extraction;Joints;Kinematics;Paints;Robots;Trajectory},
doi={10.1109/ICRA.2014.6907098},
ISSN={1050-4729},
month={May},}