Improving TaskParameterised Movement Learning Generalisation with FrameWeighted Trajectory Generation Learning from Demonstration depends on a robot learner generalising its learned model to unseen conditions. Generally, it is not feasible for a person to provide demonstrations that account for all possible variations in nontrivial 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, this paper proposes a modified TaskParameterised 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 farreaching, lowerror extrapolation abilities that are different in nature to existing learning methods. Data collected from novice users for a realworld manipulation task is then considered, where it is shown that the proposed method is able to effectively reduce grasping performance errors by sim 40% and extrapolate to unseen grasp targets under realworld conditions. These results indicate the proposed method can benefit novice users by placing less reliance on the user to provide high quality demonstration data sets.

Reinforcement Learning of Trajectory Distributions: Applications in Assisted Teleoperation and Motion Planning The majority of learning from demonstration approaches do not address suboptimal demonstrations or cases when drastic changes in the environment occur after the demonstrations were made. For example, in real teleoperation tasks, the demonstrations provided by the user are often suboptimal due to interface and hardware limitations. In tasks involving comanipulation and manipulation planning, the environment often changes due to unexpected obstacles rendering previous demonstrations invalid. This paper presents a reinforcement learning algorithm that exploits the use of relevance functions to tackle such problems. This paper introduces the Pearson correlation as a measure of the relevance of policy parameters in regards to each of the components of the cost function to be optimized. The method is demonstrated in a static environment where the quality of the teleoperation is compromised by the visual interface (operating a robot in a threedimensional task by using a simple 2D monitor). Afterward, we tested the method on a dynamic environment using a real 7DoF robot arm where distributions are computed online via Gaussian Process regression.