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.