Tactile-Based Robotic Peg Extraction Dataset

This dataset provides robotic and tactile sensor data captured using two multi-modal tactile sensing (BioIn-Tacto [1, 2]) modules mounted on the end-effector of an OpenManipulator X. The sensor includes barometric and MARG (Magnetic, Angular Rate, and Gravity) data to support research in tactile manipulation. The dataset was collected in teleoperation experiments involving the extraction of differently shaped pegs from a base with holes using a robotic manipulator arm. The total number of extraction episodes in the dataset is 96. The dataset also includes Reinforcement Learning pre-trained data. The dataset can be used to pre-train a reinforcement learning model to perform peg-in-hole tasks and to study how pre-training affects a manipulator’s ability to infer tactile signals and improve success rates of the manipulator. The data is organized into folders representing each object runs: Data/ └RL/ │ └Object<1|2|3|> │ └recordstep_<timestr>_object<1|2|3>_pretained_<ID>.csv └Teleop/ ├csv2dataframe.py ├README.md └──csv/ └Object<1|2|3|>/ └robot_O<1|2|3>_T<n>_A<0|45|90|135|180>_<timestr>/ ├imu1_data_raw.csv ├imu1_mag.csv ├imu2_data_raw.csv ├imu2_mag.csv ├imu_data1.csv ├imu_data2.csv ├joint_states.csv ├m_baros_serial1.csv ├m_baros_serial2.csv ├pressure_viz_left.csv ├pressure_viz_right.csv ├raw_barometers_teensy1.csv ├raw_barometers_teensy2.csv ├raw_imu_teensy1.csv ├raw_imu_teensy2.csv ├robot_instruction.csv ├tf.csv └tf_static.csv - csv2dataframe.py: Converts the data into dataframes - Object<1|2|3|>/: Three folders with data from each object - recordstep_<timestr>_object<1|2|3>_pretained_<ID>: Files with RL data for each object. - robot_O<1|2|3>_T<n>_A<0|45|90|135|180>_<timestr>/: Data dollected from each object at different angles. [1] T. E. Alves de Oliveira, A. -M. Cretu and E. M. Petriu, "Multimodal Bio-Inspired Tactile Sensing Module," in IEEE Sensors Journal, vol. 17, no. 11, pp. 3231-3243, 1 June1, 2017, https://doi.org/10.1109/JSEN.2017.2690898. [2] T. E. Alves de Oliveira, V. Prado da Fonseca, BioIn-Tacto: A compliant multi-modal tactile sensing module for robotic tasks, HardwareX, Volume 16, 2023, e00478, ISSN 2468-0672, https://doi.org/10.1016/j.ohx.2023.e00478.本数据集提供了安装在OpenManipulator X机械臂末端执行器上的两款多模态触觉传感（BioIn-Tacto）模块所采集的机器人与触觉传感器数据。该传感器集成了气压数据与MARG（磁力、角速度与重力）数据，可支撑触觉操控相关研究。数据集采集自遥操作实验：实验中使用机械臂从带孔基座中拔出不同形状的销钉，共包含96次拔插任务回合。数据集还包含强化学习（Reinforcement Learning）预训练数据，可用于预训练面向销钉插入孔位任务的强化学习模型，以及研究预训练对机械臂推断触觉信号、提升任务成功率的影响。 数据按不同对象的运行任务划分为层级文件夹，具体结构如下： Data/ └RL/ │ └Object<1|2|3|> │ └recordstep_<timestr>_object<1|2|3>_pretained_<ID>.csv └Teleop/ ├csv2dataframe.py ├README.md └──csv/ └Object<1|2|3|>/ └robot_O<1|2|3>_T<n>_A<0|45|90|135|180>_<timestr>/ ├imu1_data_raw.csv ├imu1_mag.csv ├imu2_data_raw.csv ├imu2_mag.csv ├imu_data1.csv ├imu_data2.csv ├joint_states.csv ├m_baros_serial1.csv ├m_baros_serial2.csv ├pressure_viz_left.csv ├pressure_viz_right.csv ├raw_barometers_teensy1.csv ├raw_barometers_teensy2.csv ├raw_imu_teensy1.csv ├raw_imu_teensy2.csv ├robot_instruction.csv ├tf.csv └tf_static.csv 各文件与文件夹说明如下： - csv2dataframe.py：用于将原始数据转换为数据帧 - Object<1|2|3|>/：包含三类不同测试对象的数据集文件夹 - recordstep_<timestr>_object<1|2|3>_pretained_<ID>.csv：对应各测试对象的强化学习预训练数据文件 - robot_O<1|2|3>_T<n>_A<0|45|90|135|180>_<timestr>/：存储不同角度下各测试对象的采集数据 参考文献： [1] T. E. Alves de Oliveira, A.-M. Cretu 与 E. M. Petriu, 《多模态仿生触觉传感模块》，发表于IEEE Sensors Journal，第17卷第11期，第3231-3243页，2017年6月1日，https://doi.org/10.1109/JSEN.2017.2690898. [2] T. E. Alves de Oliveira, V. Prado da Fonseca, 《BioIn-Tacto：面向机器人任务的柔顺多模态触觉传感模块》，HardwareX，第16卷，2023年，e00478，ISSN 2468-0672，https://doi.org/10.1016/j.ohx.2023.e00478.