---
license: mit
tags:
- physical-ai-interpretability-sae
- LeRobot
- Robotics
datasets:
- villekuosmanen/drop_footbag_into_dice_tower
- villekuosmanen/drop_footbag_into_dice_tower_continuous
- villekuosmanen/dAgger_drop_footbag_into_dice_tower_1.0.0
- villekuosmanen/dAgger_drop_footbag_into_dice_tower_1.1.0
- villekuosmanen/dAgger_drop_footbag_into_dice_tower_1.2.0
- villekuosmanen/dAgger_drop_footbag_into_dice_tower_1.3.0
- villekuosmanen/dAgger_drop_footbag_into_dice_tower_1.4.0
- villekuosmanen/dAgger_drop_footbag_into_dice_tower_1.5.0
- villekuosmanen/dAgger_drop_footbag_into_dice_tower_1.6.0
- villekuosmanen/eval_footbag_11Sep
library_name: physical-ai-interpretability
---

# Sparse Autoencoder (SAE) Model

This model is a Sparse Autoencoder trained for interpretability analysis of robotics policies using the LeRobot framework.

## Model Details

- **Architecture**: Multi-modal Sparse Autoencoder
- **Training Dataset**: `[villekuosmanen/drop_footbag_into_dice_tower, villekuosmanen/drop_footbag_into_dice_tower_continuous, villekuosmanen/dAgger_drop_footbag_into_dice_tower_1.0.0, villekuosmanen/dAgger_drop_footbag_into_dice_tower_1.1.0, villekuosmanen/dAgger_drop_footbag_into_dice_tower_1.2.0, villekuosmanen/dAgger_drop_footbag_into_dice_tower_1.3.0, villekuosmanen/dAgger_drop_footbag_into_dice_tower_1.4.0, villekuosmanen/dAgger_drop_footbag_into_dice_tower_1.5.0, villekuosmanen/dAgger_drop_footbag_into_dice_tower_1.6.0, villekuosmanen/eval_footbag_11Sep]`
- **Base Policy**: LeRobot ACT policy
- **Layer Target**: `model.encoder.layers.3.norm2`
- **Tokens**: 77
- **Token Dimension**: 128
- **Feature Dimension**: 12320
- **Expansion Factor**: 1.25

## Training Configuration

- **Learning Rate**: 0.0001
- **Batch Size**: 16
- **L1 Penalty**: 0.3
- **Epochs**: 15
- **Optimizer**: adam

## Usage

```python
from physical_ai_interpretability.sae import load_sae_from_hub

# Load model from Hub
model = load_sae_from_hub("villekuosmanen/drop_footbag_into_dice_tower_ood_sae_success")

# Or load using builder
from physical_ai_interpretability.sae import SAEBuilder
builder = SAEBuilder(device='cuda')
model = builder.load_from_hub("villekuosmanen/drop_footbag_into_dice_tower_ood_sae_success")
```

## Out-of-Distribution Detection

This SAE model can be used for OOD detection with LeRobot policies:

```python
from physical_ai_interpretability.ood import OODDetector

# Create OOD detector with Hub-loaded SAE
ood_detector = OODDetector(
    policy=your_policy,
    sae_hub_repo_id="villekuosmanen/drop_footbag_into_dice_tower_ood_sae_success"
)

# Fit threshold and use for detection
ood_detector.fit_ood_threshold_to_validation_dataset(validation_dataset)
is_ood, error = ood_detector.is_out_of_distribution(observation)
```

## Files

- `model.safetensors`: The trained SAE model weights
- `config.json`: Training and model configuration
- `training_state.pt`: Complete training state (optimizer, scheduler, metrics)
- `ood_params.json`: OOD detection parameters (if fitted)
```

## Framework

This model was trained using the [physical-ai-interpretability](https://github.com/your-repo/physical-ai-interpretability) framework with [LeRobot](https://github.com/huggingface/lerobot).