Improving Task-Parameterised Movement Learning Generalisation with Frame-Weighted Trajectory Generation

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.Comment: 8 pages, 6 figures, submitted to 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Demonstration Tray Data

Data stored in a zipped ~1.5Gb MATLAB .mat file. --- See [Sena, Howard 2019] for full details on data collection, summary below (link 1 in references). --- Data formatted to follow similar internal 's' structure as [Calinon 2015] (link 2 in references). ---- Loads a struct 'groups'. Contains data from a 4 x 3 mixed ANOVA style study. Condition 1: No FeedbackCondition 2: Replay FeedbackCondition 3: Batch FeedbackCondition 4: Selected Feedback See [Sena, Howard 2019] for full details on test conditions (link 1 in references). ---- Data collected from novice participants using a Rethink Robotics Sawyer collaborative robot to perform a horticultural tray sorting manipulation task. --- 4 groups, one for each condition.Each group contains:- demos : Set of data collected from users-- Each row in demos represents a participant.-- Each column represents an attempt at the assigned condition.-- Contains sub structs containing the experiment data for each attempt (demonstration data trajectories, etc.)- repos : Set of data generated with TP-GMR model, as described in [Sena, Howard 2019]-- Rows and columns same as demos, but all data is generated from the model.-- Same sub structs as demos, except there will be 100 trajectories (one for each target plant in the experiment tray). --- This experiment was conducted with ethical approval granted by King's College London Research Ethics Committee under LRS-17/18-5549