Robot Programming by Demonstration: A Probabilistic Approach
Sylvain Calinon
Abstract
ACKNOWLEDGMENT INTRODUCTION Contributions Organization of the book Review of Robot Programming by Demonstration (PBD) Current state of the art in PbD SYSTEM ARCHITECTURE Illustration of the proposed probabilistic approach Encoding of motion in a Gaussian Mixture Model (GMM) Encoding of motion in Hidden Markov Model (HMM) Reproduction through Gaussian Mixture Regression (GMR) Reproduction by considering multiple constraints Learning of model parameters Reduction of dimensionality and latent space projection Model selection and initialization Regularization of GMM parameters Use of prior information to speed up the learning process Extension to mixture models of varying density distributions Summary of the chapter COMPARISON AND OPTIMIZATION OF THE PARAMETERS Optimal reproduction of trajectories through HMM and GMM/GMR Optimal latent space of motion Optimal selection of the number of Gaussians Robustness evaluation of the incremental learning process HANDLING OF CONSTRAINTS IN JOINT SPACE AND TASK SPACE Inverse kinematics Handling of task constraints in joint spaceexperiment with industrial robot Handling of task constraints in latent spaceexperiment with humanoid robot EXTENSION TO DYNAMICAL SYSTEM AND HANDLING OF PERTURBATIONS Proposed dynamical system Influence of the dynamical system parameters Experimental setup Experimental results TRANSFERRING SKILLS THROUGH ACTIVE TEACHING METHODS Experimental setup Experimental results Roles of an active teaching scenario USING SOCIAL CUES TO SPEED UP THE LEARNING PROCESS Experimental setup Experimental results DISCUSSION, FUTURE WORK AND CONCLUSIONS Advantages of the proposed approach Failures and limitations of the proposed approach Further issues Final words REFERENCES INDEX