ACT Baseline for RH20T Dataset

Example for training ACT on RH20T dataset. This project is built on top of ACT.

Installation

git clone https://github.com/rh20t/act_baseline.git
cd act_baseline
pip install -r requirements.txt

Loading data with cleaned data list

Sometimes you may encounter data loading errors due to NaN annotations in a few amount of scenes. We recommand loading data with rh20t_cleaned_data.json to reduce program errors and accelerate data processing.

For each scene, this file contains a subset of the original video, which is implemented by removing redundant frames according to their tcp differences. The selected frames is saved as timestamps like "1629432627550".

The structure in the file is as follows:

{
    "RH20T_cfg1": {
        "task_0001_user_0001_scene_0001_cfg_0001": {
            "cam_035622060973": [
                1629432627550,
                1629432629589,
                1629432629779,
                1629432630013,
                1629432630132,
                1629432630233,
                ...
            ],
            "cam_038522062547": [
                1629432627038,
                1629432629519,
                1629432629791,
                1629432629904,
                1629432630136,
                1629432630260,
                ...
            ],
            ...
        },
        "task_0001_user_0001_scene_0002_cfg_0001": {
            ...
        },
        ...
    },
    "RH20T_cfg2": {
        ...
    },
    ...
    "RH20T_cfg7": {
        ...
    }
}

Download rh20t_cleaned_data.json here and put it under dataset folder. The usage is shown in dataset/rh20t.py.

Pretrain on RH20T

Train the ACT model on RH20T by running the following command:

python train.py --task_name [TASK_NAME] --ckpt_dir [YOUR_LOG_DIR] --dataset_root [RH20T_DIR] --batch_size 24 --seed 233 --num_epoch 50 --save_epoch 5 --lr 1e-5 --kl_weight 10 --chunk_size 20 --hidden_dim 512 --dim_feedforward 3200

[TASK_NAME] is the task ids in RH20T, like "task_0001". You can modify this variable to change the task to train on. The file command_pretrain.sh shows a more detailed example.

Finetune on your own data

To conduct real robot experiments in your environment, a finetune step is highly recommanded. You can modify the dataset code and training code, and fintune the pretrained model on your own data. Note that action trunking step is required during inference, and you can refer to the original ACT implementation.