Processing Collected Data

Once you collect some new data on the Stick, you need to process it into a dataset before you can train policies on it. This step will help you get started on that.

Clone the Repo

git clone [email protected]:notmahi/dobb-e
cd dobb-e/stick-data-collection

Installation

• On your machine, in a new conda/virtual environment

  mamba env create -f conda_env.yaml

Usage

For extracting a single environment:

1. Compress video taken from the Record3D app:

   

   Export Data
2. Get the files on your machine.

   1. Using Google drive:

      1. [Only once] Generate Google Service Account API key to download from private folders on Google Drive. There are some instructions on how to do so in this Stackoverflow link https://stackoverflow.com/a/72076913

      2. [Only once] Rename the .json file to client_secret.json and put it in the same directory as gdrive_downloader.py

      3. Upload .zip file into its own folder on Google Drive, and copy folder_id from URL to put it in the GDRIVE_FOLDER_ID in the ./do-all.sh file.

   2. Manually:

      • Comment out the GDRIVE_FOLDER_ID line from ./do-all.sh and create the following hierarchy locally

        dataset/
        |--- task1/
        |------ home1/
        |--------- env1/
        |------------ {data_file}.zip
        |--------- env2/
        |------------ {data_file}.zip
        |--------- env.../
        |------------ {data_file}.zip
        |------ home2/
        |------ home.../
        |--- task2/
        |--- task.../

      • The .zip files should contain .r3d files exported from the Record3D app in the previous step.

3. Modify required variables in do-all.sh.

   1. TASK_NO task id, see gdrive_downloader.py for more information.

   2. HOME name or ID of the home.

   3. ROOT_FOLDER folder where the data is stored after downloading.

   4. EXPORT_FOLDER folder where the dataset is stored after processing. Should be different from ROOT_FOLDER.

   5. ENV_NO current environment number in the same home and task set.

   6. GRIPPER_MODEL_PATH path to the gripper model. It should be in the github repo already, and can be downloaded from http://dl.dobb-e.com/models/gripper_model.pth.

4. Change current working directory to local repository root folder and run

   ./do-all.sh

5. Split the extracted data to include a validation set for each environment. The data should follow the following hierarchy: (Be sure change the corresponding paths in r3d_files.txt to include “_val”)\

   dataset/
   |--- task1/
   |------ home1/
   |--------- env1/
   |--------- env1_val/
   |--------- env2/
   |--------- env2_val/
   |--------- env.../
   |------ home2/
   |--------- env1/
   |--------- env1_val/
   |--------- env.../
   |------ home.../
   |--- task2/
   |------ home1/
   |--------- env1/
   |--------- env1_val/
   |--------- env.../
   |------ home2/
   |--------- env1/
   |--------- env1_val/
   |--------- env.../
   |------ home.../
   |--- task.../
   |--- r3d_files.txt