metadata
license: mit
task_categories:
- robotics
Dataset of Reactive Diffusion Policy
Contents
Description
This is the raw and postprocessed dataset used in the paper Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation.
Structure
We offer two versions of the dataset: one is the full dataset used to train the models in our paper, and the other is a mini dataset for easier examination. Both versions include raw and postprocessed subsets of peeling, wiping and lifting.
Each raw subset is structured as follows:
subset_name
├── seq_01.pkl
├── seq_02.pkl
├── ...
Note that we split the full raw lifting subset into 2 parts due to file size restrictions.
Each postprocessed subset is stored in Zarr format, which is structured as follows:
├── action (25710, 4) float32
├── external_img (25710, 240, 320, 3) uint8
├── left_gripper1_img (25710, 240, 320, 3) uint8
├── left_gripper1_initial_marker (25710, 63, 2) float32
├── left_gripper1_marker_offset (25710, 63, 2) float32
├── left_gripper1_marker_offset_emb (25710, 15) float32
├── left_gripper2_img (25710, 240, 320, 3) uint8
├── left_gripper2_initial_marker (25710, 25, 2) float32
├── left_gripper2_marker_offset (25710, 25, 2) float32
├── left_gripper2_marker_offset_emb (25710, 15) float32
├── left_robot_gripper_force (25710, 1) float32
├── left_robot_gripper_width (25710, 1) float32
├── left_robot_tcp_pose (25710, 9) float32
├── left_robot_tcp_vel (25710, 6) float32
├── left_robot_tcp_wrench (25710, 6) float32
├── left_wrist_img (25710, 240, 320, 3) uint8
├── right_robot_gripper_force (25710, 1) float32
├── right_robot_gripper_width (25710, 1) float32
├── right_robot_tcp_pose (25710, 9) float32
├── right_robot_tcp_vel (25710, 6) float32
├── right_robot_tcp_wrench (25710, 6) float32
├── target (25710, 4) float32
└── timestamp (25710,) float32
Usage
Follow the README in our GitHub repo to postprocess the raw data and train the model.