MetaFold: Language-Guided Multi-Category Garment Folding Framework via Trajectory Generation and Foundation Model

1. Environment Setup

Use Isaac Sim 4.1 as the main simulation environment.
Update the Python interpreter of your virtual environment to use the python.sh script provided in the Isaac Sim root directory.
- Example:
```
/path/to/isaac-sim/python.sh
```

2. Dependency Installation

pip install -r requirements.txt

Install the required dependencies, including but not limited to:

ManiFM

Ensure all packages are correctly installed within the Isaac Sim Python environment.

3. Model Preparation

The model file is split into multiple parts. Merge them into a single .pth file:

cat data/model_1113_199.pth.part_* > data/model_1113_199.pth

Usage Guide

This section describes how to run experiments, either in single-instance mode or batch mode.

1. Single-Instance Run

Use the following script to run a single experiment:

python start_single.py

Input Configuration

Pay attention to the input parameters in start_single.py:

# input
# --------------------------------------------------------------------------------------------
descriptions = [...]          # Folding instruction sentences
cloth_config_path = "./data/Cloth-Simulation/Configs/Pants.json"   # Cloth configuration file
cloth_root = "./data/Cloth-Simulation/Assets/eval/"                # Root directory of cloth assets
cloth_name = "5PS_2"                                               # Name of the cloth folder

Cloth Configuration File

Example of a cloth configuration file:

{
    "type": "Pants",
    "scale": [0.5, 0.5, 0.5],
    "stage_num": 2,                   # Total number of folding stages (actions)
    "frame_per_stage": [60, 60],      # Frame count for each stage
    "force_per_stage": [2, 2],        # Force count for each stage
    "step_turn": [1, 1],              # Currently unused
    "frames_per_step": 10,            # Frames per step (for closed-loop prediction)
    "threshold": 0.05                 # Currently unused
}

Batch Run

Use the following script to run a single experiment:

python start_batch.py

Input Configuration

Pay attention to the input parameters in start_batch.py:

# input
# --------------------------------------------------------------------------------------------
batch_input_path = "./data/Cloth-Simulation/Batch_Input/batch_input.json"
instructions_path = "./data/Cloth-Simulation/Batch_Input/Instructions.json"                                               # Name of the cloth folder

Cloth Configuration File

Example of a cloth configuration file:

[
    {
        "cloth_root": "./data/Cloth-Simulation/Assets/eval"   # Initial entry: specifies the root directory for cloth assets
    },
    {
        "cloth_type": "No-sleeve",  # Cloth type; must match a key in Instructions.json
        "cloth_name": "0DSNS_0"     # Name of the cloth folder
    },
    {
        "cloth_type": "Pants",
        "cloth_name": "3PS_0"
    }
]

Instructoin Configuration File

{
    "No-sleeve": {  
        "cloth_config_path": "./data/Cloth-Simulation/Configs/No-sleeve.json",  # Path to cloth configuration file
        "descriptions": [                                                      # Folding instructions
            "fold the no-sleeve bottom-up"
        ]
    },
    "Long-sleeve": {
        "cloth_config_path": "./data/Cloth-Simulation/Configs/Long-sleeve.json",
        "descriptions": [
            "Please fold Long-Sleeve top from the left sleeve",
            "Please fold Long-Sleeve top from the right sleeve",
            "Please fold Long-Sleeve top from the bottom-up"
        ]
    }
}

Output

After the simulation finishes, check the output directory:

./isaac_sim/output

Each experiment will generate a corresponding folder:

.../mesh*

Inside, you will find two point cloud files:

initial_pcd.txt — Point cloud of the initial state

fin_pcd.txt — Point cloud of the final state

During the simulation, you can monitor the predicted trajectories in:

./vis_pc_eval_real.png

./vis_pc2_eval_real.png

These files visualize trajectory predictions across different steps of the folding process.

NUS-LinS-Lab/MetaFold

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