DRIVE Datasets

This page is dedicated to sharing our datasets for our paper entitled "DRIVE: Data-driven Robot Input Vector Exploration". The datasets for each experiment are available in post-processed Pandas data frames. The protocol code used to automate dataset gathering is available open-source.

• Husky Snow Dataframe
• Husky Tile Dataframe
• HD2 Snow Dataframe
• HD2 Tile Dataframe
• Warthog Gravel Dataframe
• Warthog Ice Dataframe

For each Dataframe, each row represents a two-second training step. Each set of three consecutive rows represent a 6-second training interval, consisting of one transitory training step and two steady training steps. For each column, we have the data sampled at a rate of 20 Hz, leading to 40 timesteps for each training step. We provide our Ground-truth localization as well as smoothed ground-truth velocity and acceleration for training models. We also provide commanded and measured wheel and body velocities. Lastly, we provide the pre-computed single-step body slip velocity, which can be used to train learned slip models. To extract a specific set of data from the Dataframe, the following Python code can be used :

idd_body_vel_x_str_list = []
idd_body_vel_y_str_list = []
idd_body_vel_yaw_str_list = []
for i in range(0, 40):
    str_idd_vel_x_i = 'idd_vel_x_' + str(i)
    str_idd_vel_y_i = 'idd_vel_y_' + str(i)
    str_idd_vel_yaw_i = 'idd_vel_yaw_' + str(i)
    idd_body_vel_x_str_list.append(str_idd_vel_x_i)
    idd_body_vel_y_str_list.append(str_idd_vel_y_i)
    idd_body_vel_yaw_str_list.append(str_idd_vel_yaw_i)
idd_body_vel_x_array = dataframe[idd_body_vel_x_str_list].to_numpy()
idd_body_vel_y_array = dataframe[idd_body_vel_y_str_list].to_numpy()
idd_body_vel_yaw_array = dataframe[idd_body_vel_yaw_str_list].to_numpy()