# LaundryNauts — LeHome Challenge 2026 Submission

**Team:** LaundryNauts | **Registration ID:** r20 | **Institution:** Northwestern University

## Method

We fine-tune [SmolVLA](https://huggingface.co/lerobot/smolvla_base) on 1,000 bimanual garment-folding episodes collected in the LeHome simulator. The model takes three RGB camera views (top, left wrist, right wrist) and 12-DOF joint state as input, and outputs 12-DOF dual-arm joint angle commands.

- **Policy checkpoint + source:** `LaundryNauts/smolvla_augmented` (HuggingFace)
- **Training dataset:** `LaundryNauts/AugmentedDataset` (HuggingFace)

---

## Evaluation Setup

### Requirements
- Linux x86_64, Python 3.11, `uv` package manager
- CUDA GPU
- Isaac Sim 5.1.0 (via the LeHome base repo)

### Step 1 — Install the LeHome Base Environment

Follow the official LeHome setup instructions to install Isaac Lab and the `lehome` package.

### Step 2 — Authenticate with HuggingFace

```bash
huggingface-cli login --token <hf_token_provided_in_submission>
```

### Step 3 — Download Policy, Source Code, and Dataset Metadata

```bash
# Policy checkpoint
huggingface-cli download LaundryNauts/smolvla_augmented --local-dir outputs/smolvla_new

# Custom eval scripts (place into your lehome repo)
huggingface-cli download LaundryNauts/smolvla_augmented \
    scripts/eval_policy/lerobot_policy.py \
    scripts/eval_policy/base_policy.py \
    scripts/eval_policy/registry.py \
    --local-dir .

# Dataset metadata (only meta/ is required, not the full data)
huggingface-cli download LaundryNauts/AugmentedDataset \
    --repo-type dataset --local-dir Datasets/augmented_data
```

### Step 4 — Download Sim Assets

```bash
huggingface-cli download lehome/asset_challenge --repo-type dataset --local-dir Assets
```

### Step 5 — Run Evaluation

**Single garment type:**
```bash
export TMPDIR=/tmp/$(whoami) && mkdir -p $TMPDIR

LIVESTREAM=2 python -m scripts.eval \
    --policy_type lerobot \
    --policy_path outputs/smolvla_new \
    --garment_type top_long \
    --dataset_root Datasets/augmented_data \
    --num_episodes 2
```

**All garment types:**
```bash
for type in top_long top_short pant_long pant_short; do
    LIVESTREAM=2 python -m scripts.eval \
        --policy_type lerobot \
        --policy_path outputs/smolvla_new \
        --garment_type $type \
        --dataset_root Datasets/augmented_data \
        --num_episodes 2 \
        2>&1 | tee -a results_all.txt
done
```

---

## Self-Reported Results

See `results.txt` for full per-garment breakdown.

| Category   | Success Rate  |
|------------|--------------|
| Top Long   | 16/24 = 66.7% |
| Top Short  | 6/24  = 25.0% |
| Pant Long  | 13/24 = 54.2% |
| Pant Short | 18/24 = 75.0% |
| **Overall** | **53/96 = 55.2%** |