Improving Task-Parameterized Movement Learning with Frame-Weighted Trajectory Generation

TY - CONF

T1 - Improving Task-Parameterized Movement Learning with Frame-Weighted Trajectory Generation

AU - Sena, Aran

AU - Michael, Brendan

AU - Howard, Matthew Jacob William

N1 - Conference code: 32

PY - 2019/11

Y1 - 2019/11

N2 - 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 ∼30% and extrapolate to unseen grasp targets under realworld 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.

AB - 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 ∼30% and extrapolate to unseen grasp targets under realworld 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.

KW - Learning from Demonstrations

KW - GMM

KW - Trajectory Planning

KW - Machine Learning

KW - Robotics

KW - Imitation Learning

KW - Task Parameterised Learning

KW - Task Parameterized Learning

KW - Manipulation

M3 - Paper

T2 - IEEE/RSJ International Conference on Intelligent Robots and Systems

Y2 - 4 November 2019 through 8 November 2019

ER -