Collection.md

Data Collection

UniVTAC's data synthesizer enables fully automated data collection by executing scripted manipulation policies (defined in the envs/ directory) in combination with the cuRobo motion planner. Data collection is configured through task-shared configuration files in task_config/, which define parameters such as the target tactile sensor type, observation modalities, texture randomization, and the number of episodes to collect.

The pipeline iterates over random seeds, executing the scripted policy for each seed and saving observation data on success. Failed seeds are skipped automatically, and progress is tracked in suc_map.txt to support resuming from interruptions. The entire process is fully automated — just run a single command to get started.

Running the following command will start data collection for the specified task:

bash collect_data.sh ${task_name} ${config_name} ${gpu_id}
# Example: bash collect_data.sh lift_bottle demo 0

For faster collection with multiple parallel simulation workers: (Note: the parallel collection is implemented with Python's multiprocessing, so multiple Isaac Sim Apps will be launched on the same time)

bash parallel_collect.sh ${task_name} ${config_name} ${gpu_id} [num_processes]
# Example: bash parallel_collect.sh lift_bottle demo 0 3

All available task_name options correspond to Python modules in the envs/ directory (e.g., lift_bottle, insert_HDMI, pull_out_key, grasp_classify, etc.). The config_name parameter specifies a YAML configuration file in task_config/ (without the .yml extension). The gpu_id parameter specifies which GPU to use (multiple GPUs are supported).

Task Configuration

Field	Type	Default	Description
save_dir	str	./data	Root directory for saving collected data.
decimation	int	1	Physics sub-stepping factor.
save_frequency	int	2	Save observations every N simulation steps.
video_frequency	int	2	Record video frames every N steps.
render_frequency	int	0	Render GUI every N steps (0 = headless).
random_texture	bool	false	Enable random texture domain randomization.
use_seed	bool	true	Use deterministic seeding for reproducibility.
episode_num	int	100	Number of successful episodes to collect.
sensor_type	str	gsmini	Tactile sensor type: gsmini, gf225, or xensews.
observations	dict	—	Which observation modalities to record (see below).

Data Structure

After data collection is completed, the collected data will be stored under data/${config_name}/${task_name}/:

• Each episode's observation and action data are saved as an individual HDF5 file in the hdf5/ directory.
• Visualization videos of each episode (combining camera and tactile views) can be found in the video/ directory.
• Per-episode metadata (step counts, timing, success/failure results) is stored in metadata.json.
• The suc_map.txt and scene/ directory are auxiliary outputs generated during the data collection process.

Below is the structure of the saved observation data for each episode (stored in HDF5 format). HDF5Handler in envs/utils/data.py can be used to read and write this data format:

{
    "actor": {
        "prism": "np.ndarray(7,)",
        "prism_base": "np.ndarray(7,)",
        "slot": "np.ndarray(7,)"
    },
    "atom": {
        "id": "type: <class \"numpy.int64\">",
        "tag": "type: <class \"numpy.bytes_\">"
    },
    "embodiment": {
        "ee": "np.ndarray(7,)",
        "joint": "np.ndarray(9,)"
    },
    "observation": {
        "head": {
            "rgb": "np.ndarray(270, 480, 3)"
        },
        "wrist": {
            "rgb": "np.ndarray(270, 480, 3)"
        }
    },
    "step": "type: <class \"numpy.int64\">",
    "tactile": {
        "left_tactile": {
            "depth": "np.ndarray(240, 320)",
            "marker": "np.ndarray(2, 63, 2)",
            "pose": "np.ndarray(7,)",
            "rgb": "np.ndarray(240, 320, 3)",
            "rgb_marker": "np.ndarray(240, 320, 3)"
        },
        "right_tactile": {
            "depth": "np.ndarray(240, 320)",
            "marker": "np.ndarray(2, 63, 2)",
            "pose": "np.ndarray(7,)",
            "rgb": "np.ndarray(240, 320, 3)",
            "rgb_marker": "np.ndarray(240, 320, 3)"
        }
    }
}