Improving Task-Parameterised Movement Learning Generalisation with Frame-Weighted Trajectory Generation
This work addresses the challenge of generalizing robot learning from demonstrations for novice users, reducing their reliance on providing high-quality data, though it appears incremental as it modifies an existing method.
The paper tackles the problem of generalizing robot movement learning to unseen conditions by proposing a modified Task-Parameterised Gaussian Mixture Regression method that weights task parameters based on data variance. It shows the method reduces grasping performance errors by ~30% in real-world tasks and enables effective extrapolation to unseen grasp targets.
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 ${\sim30\%}$ 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.