GRF-GMM: A Trajectory Optimization Framework for Obstacle Avoidance in Learning from Demonstration

Learning from demonstrations (LfD) provides a convenient pattern to teach robot to gain skills without mechanically programming. As an LfD approach, Gaussian mixture model/Gaussian mixture regression (GMM/GMR) has been widely used for its robustness and effectiveness. However, there still exist many problems of GMM when an obstacle, which is not presented in original demonstrations, appears in the workspace of robots. To address these problems, this paper presents a novel method based on Gaussian repulsive field-Gaussian mixture model (GRF-GMM) for obstacle avoidance by optimizing the model parameters. A Gaussian repulsive force is calculated through Gaussian functions and employed to work on Gaussian components to optimize the mixture distribution which is learnt from original demonstrations. Our approach allows the reproduced trajectory to keep a safe distance away from the obstacle. Finally, the feasibility and effectiveness of the proposed method are revealed through simulations and experiments.