Robot Orientation Trajectory Imitation Learning Based on Improved ProMPs
Aiming at the problem that the Probabilistic Movement Primitives(ProMPs)ignore the nonlin-ear constraints behind the data when learning robot orientation trajectory,an improved ProMPs algorithm on Riemannian manifolds is proposed.This method exploits unit quaternion to describe the robot orientation.Firstly,the learning process of ProMPs is combined with the logarithmic mapping and Maximum Likelihood Estimate on Riemannian manifolds to probabilistically model the demonstrated orientation trajectories;Sub-sequently,the exponential mapping on Riemannian manifolds is exploited to convert the ProMPs inferred trajectory into the robot orientation trajectory;Finally,the algorithm performance is evaluated based on the UR5 robot.Experiment results show that the improved ProMPs enhance the accuracy and smoothness of in-ferred unit quaternion trajectories by 56%and 35%,respectively,when compared to the original ProMPs.The improved ProMPs also allow the robot to reach the target orientation faster.
robotsimitation learningunit quaternionprobabilistic movement primitivesriemannian mani-folds
万方数据
维普
