Learning Spatio-Temporal Feature Templates from Demonstrations for Optimization Based Trajectory Generation

Abstract

Learning from demonstration (LID) is an effective method trying to generate trajectory from the demonstrations for the new specifications. We present a novel LID method by incorporating the trajectory feature metric term with the optimization-based motion planning method. The advantage of our method is that the feature of demonstrated trajectory is kept on the premise of generating feasible and specified trajectory. In order to maintain the characteristics of trajectory, spatiotemporal feature is chosen as metrics to be included in the cost function. Our method is verified through the simulation results of mini-jerk trajectories and trajectories from Pioneer3-AT wheeled mobile robot simulator platform. The experiments show that our method can balance the specification of task with the features of demonstrations.