Learning from Demonstration depends on a robot learner generalising its
learned model to unseen conditions, as it is not feasible for a person to
provide a demonstration set that accounts for all possible variations in
non-trivial tasks. While there are many learning methods that can handle
interpolation of observed data effectively, extrapolation from observed data
offers a much greater challenge. To address this problem of generalisation,
this paper proposes a modified Task-Parameterised Gaussian Mixture Regression
method that considers the relevance of task parameters during trajectory
generation, as determined by variance in the data. The benefits of the proposed
method are first explored using a simulated reaching task data set. Here it is
shown that the proposed method offers far-reaching, low-error extrapolation
abilities that are different in nature to existing learning methods. Data
collected from novice users for a real-world manipulation task is then
considered, where it is shown that the proposed method is able to effectively
reduce grasping performance errors by ā¼30% and extrapolate to unseen
grasp targets under real-world conditions. These results indicate the proposed
method serves to benefit novice users by placing less reliance on the user to
provide high quality demonstration data sets.Comment: 8 pages, 6 figures, submitted to 2019 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS