---
license: apache-2.0
tags:
- robotics
- reinforcement-learning
- metaworld
- imitation-learning
- action-chunking
library_name: pytorch
---

# ACT-MODIFIED - MetaWorld MT-1 Shelf-Place

## Model Description

This is a trained **MODIFIED Action Chunking with Transformers (ACT)** model for the MetaWorld MT-1 shelf-place-v3 task.

## Architecture

**Modified ACT** uses images in both **encoder and decoder** (visual conditioning).

- **Encoder**: Takes image features + state (joints) + action history → latent distribution  
- **Decoder**: Takes image features + state + latent sample → action chunk
- **Advantage**: Richer visual conditioning, more expressive latent space (25.43M parameters)
- **Hypothesis**: Should perform better with more training data

## Training Details

- **Task**: MetaWorld MT-1 shelf-place-v3
  - Single-task manipulation (place puck on shelf)
  - Varying object positions (randomized)
- **Observations**: 
  - State: 39-dimensional (joint positions, velocities, gripper info)
  - Images: 480×480 RGB (downsampled to 64×64 for processing)
- **Action Space**: 4D continuous [Δx, Δy, Δz, gripper]
- **Training**:
  - Demonstrations: 10 expert episodes (100% success)
  - Training samples: 4,500
  - Epochs: 50
  - Batch size: 8
  - Learning rate: 1e-4
  - Chunk size: 100 steps

## Performance

- **Success Rate**: 0% (limited training data)
- **Status**: Converged, ready for evaluation with more data

## Usage

### Installation

```bash
# Clone repo and install
git clone https://huggingface.co/aryannzzz/act-metaworld-shelf-modified
pip install torch torchvision
```

### Loading the Model

```python
import torch
from pathlib import Path

# Load checkpoint
device = 'cuda' if torch.cuda.is_available() else 'cpu'
checkpoint = torch.load('model_modified.pt', map_location=device)

# Model config is in checkpoint['config']
model_config = checkpoint['config']
print("Model configuration:", model_config)

# The checkpoint contains:
# - model_state_dict: Model weights
# - config: Model architecture config
# - training_config: Training hyperparameters
```

## Model Architecture Details

### Configuration

```json
{
  "dataset": {
    "batch_size": 8,
    "num_workers": 2,
    "val_split": 0.2
  },
  "model": {
    "joint_dim": 39,
    "action_dim": 4,
    "hidden_dim": 256,
    "latent_dim": 32,
    "n_encoder_layers": 4,
    "n_decoder_layers": 4,
    "n_heads": 8,
    "feedforward_dim": 1024,
    "dropout": 0.1
  },
  "chunking": {
    "chunk_size": 50,
    "temporal_ensemble_weight": 0.01
  },
  "training": {
    "epochs": 50,
    "learning_rate": 0.0001,
    "weight_decay": 0.0001,
    "kl_weight": 10.0,
    "grad_clip": 1.0
  },
  "env": {
    "task": "shelf-place-v3",
    "image_size": [
      480,
      480
    ],
    "action_space": 4,
    "state_space": 39
  },
  "logging": {
    "use_wandb": false,
    "log_every": 10,
    "save_every": 10
  }
}
```

## Citation

If you use this model, please cite:

```bibtex
@article{zhao2023learning,
  title={Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware},
  author={Zhao, Tony Z and Kumar, Vikash and Levine, Sergey and Finn, Chelsea},
  journal={arXiv preprint arXiv:2304.13705},
  year={2023}
}
```

## License

Apache License 2.0

---

**Uploaded**: 2025-12-11 22:12:29  
**Variant**: modified  
**Repository**: https://huggingface.co/aryannzzz/act-metaworld-shelf-modified