On Learning the Statistical Representation of a Task and Generalizing it to Various Contexts

— This paper presents an architecture for solving generically the problem of extracting the constraints of a given task in a programming by demonstration framework and the problem of generalizing the acquired knowledge to various contexts. We validate the architecture in a series of experiments, where a human demonstrator teaches a humanoid robot simple manipulatory tasks. First, the combined joint angles and hand path motions are projected into a generic latent space, composed of a mixture of Gaussians (GMM) spreading across the spatial dimensions of the motion. Second, the temporal variation of the latent representation of the motion is encoded in a Hidden Markov Model (HMM). This two-step probabilistic encoding provides a measure of the spatio-temporal correlations across the different modalities collected by the robot, which determines a metric of imitation performance. A generalization of the demonstrated trajectories is then performed using Gaussian Mixture Regression (GMR). Fin...

• Type: Conference
• Year: 2006
• Where: ICRA
• Authors: Sylvain Calinon, Florent Guenter, Aude Billard
• Keywords: Generic Latent Space, ICRA 2006, Robotics, Simple Manipulatory Tasks, Two-step Probabilistic Encoding