Semantic Trajectory Segmentation and Comparison for Robot Demonstration Learning with Different Input Devices in Virtual Reality

Training a robot through demonstration requires robust algorithms capable of processing provided trajectories to generate high-quality task executions. However, the success of this process is highly dependent on the quality of the trajectories. Poor-quality trajectories hinder the ability of algorithms to learn and generalize effectively, while high-quality trajectories can improve learning outcomes, reducing the need for overly complex algorithms. In this work, we propose an initial method for comparing sets of semantically annotated trajectories used for robot demonstration. To evaluate the proposed methodologies, we recorded trajectories of 60 participants in a study setup in Virtual Reality where each participant tested one of three different control-visualization compositions. We use classical approaches such as Dynamic Time Warping and Discrete Frechet-Distance to measure the similarity between segments of recorded trajectories and show that different input devices and visualization combinations affect the resulting trajectory metrics.