Action effect generalization, recognition and execution through Continuous Goal-Directed Actions
2014 IEEE International Conference on Robotics and Automation (ICRA)
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
... ct 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.