evelyd/paper_delia_2024_ral_physics-informed_trajectory_generation_dataset

Trajectory Generation with Physics-Informed Learning and Drift Mitigation

Evelyn D'Elia, Paolo Maria Viceconte, Lorenzo Rapetti, Diego Ferigo, Giulio Romualdi, Giuseppe L'Erario, Raffaello Camoriano, and Daniele Pucci

Submitted to IEEE Robotics and Automation Letters (RA-L)

📂 Dataset

The dataset is organized in folders. The layout is explained below.

• The mocap/D2 folder contains the raw motion capture data, acquired via a human using the Xsens motion tracking suit. Within D2 there are 5 subfolders for each data subset: forward, backward, side, diagonal, and mixed walking. each subfolder contains a log file which can be retargeted onto the robot.
• The retargeted_mocap folder contains the text files of the retargeted mocap data, in both original (D2) and mirrored (D2_mirrored) form.
• The IO_features folder contains the text files of the extracted features (data and labels) to be used in training. There are 4 subfolders which correspond to the original and mirrored inputs and the original and mirrored outputs.
• The onnx folder contains the onnx files corresponding to trained models from each PI loss value as well as from the original ADHERENT framework.
• The reproduce_plots folder contains .mat files with all the data needed to reproduce the selected plots.