Dataset associated to the "ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots" paper (manuscript DOI: 10.1109/LRA.2022.3141658)

This dataset contains data accompanying the work:

@ARTICLE{9676410,
author={Viceconte, Paolo Maria and Camoriano, Raffaello and Romualdi, Giulio and Ferigo, Diego and Dafarra, Stefano and Traversaro, Silvio and Oriolo, Giuseppe and Rosasco, Lorenzo and Pucci, Daniele},
journal={IEEE Robotics and Automation Letters},
title={ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots},
year={2022},
volume={7},
number={2},
pages={2779-2886},
doi={10.1109/LRA.2022.3141658}}

The dataset is organized in folders, whose content can be summarized as follows:
- mocap: motion capture data collected from human motion
- retargeted_mocap: motion capture data retargeted on the robot
- IO_features: input and output features extracted from the retargeted mocap data to train the trajectory generator
- training_D2_D3_subsampled_mirrored_4ew_98%: training data
- inference: data collected while generating trajectories
- trajectory_control_simulation: data collected while controlling trajectories in simulation
- trajectory_control_real_robot: data collected while controlling trajectories on the real robot
- additional_figures: additional data to reproduce some figures in the paper and portions of the supplementary video

A more detailed description of the content of each folder is provided in the README.txt file included in the dataset.