---
license: apache-2.0
base_model: Qwen/Qwen2.5-VL-3B-Instruct
tags:
  - robotics
  - vision-language-action
  - vla
  - qwen2_5_vl
  - lerobot
  - libero
---

# BaseVLA — LIBERO 4-suite (Qwen2.5-VL-3B) with domain augmentation

Plain behaviour cloning baseline: **no V-JEPA, no latent alignment, no
decorrelation**. Just `L1(action)` on the LIBERO 4-suite mix, with domain
augmentation switched on. Trained for 100,000 steps.

This is the control condition for a study on viewpoint↔task spurious
correlation — the "what does augmentation alone buy you?" cell.

## Data

The four standard LIBERO suites, sampled with equal weight per suite
(`weight / n_frames`, so suite size does not bias the mix):

| suite | episodes | frames |
|---|---|---|
| libero-spatial | 432 | 52,970 |
| libero-object | 454 | 66,984 |
| libero-goal | 428 | 52,042 |
| libero-10 | 379 | 101,469 |
| **total** | **1,693** | **273,465** |

Front camera (`observation.images.image`) only; the wrist camera is dropped.
10 fps, 8-D state, 7-D action.

## Architecture

```
Qwen2.5-VL-3B + LoRA ─ forward_full_hidden ─┬─ Head A → z_a ─┐
                                            └─ Head B → z_b ─┴─ concat → ResNetActionHead → 10×7

L = L1(action)        ← that is the whole objective
```

| | |
|---|---|
| LoRA | r=32 |
| Heads | dual `AttentiveLatentHead`, proj 4096, 8 queries, depth 2 |
| Inputs | 1 RGB frame @224 + task string + 8-D state |
| Output | 10-step action chunk (1.0 s @ 10 fps) |
| Optimiser | AdamW, lr 5e-5, batch 32, bf16 |
| **Augmentation** | **ColorJitter/SharpnessJitter ×2 + DomainRandomization p=0.7** |

Augmentation detail — applied to the context frame only:
lighting gain 0.3–2.0, sensor noise ISO 1–4, random crop, warping
(corner shift 0.08–0.20), each drawn independently per sample.

## Training curve

| step | loss |
|---|---|
| 10,000 | 0.0823 |
| 20,000 | 0.0731 |
| 30,000 | 0.0655 |
| 50,000 | 0.0537 |
| 70,000 | 0.0492 |
| **100,000** | **0.0479** |

17 h 28 min on one A100 80GB.

> **Not evaluated on a robot or in simulation.** The number above is a
> training-set L1 on normalised actions. No LIBERO rollout success rate is
> reported here, and rollout is what actually matters for this benchmark.

## Contents

`checkpoint.pt` holds `policy` (full VLM incl. LoRA), `latent_head`,
`free_latent_head`, `action_head`, `optimizer`, `agg_stats` (the normalisation
statistics used), and `args` (the exact CLI configuration).

Load with:

```python
import torch
ck = torch.load("checkpoint.pt", map_location="cpu", weights_only=False)
ck["step"]        # 100000
ck["args"]        # full training config
ck["agg_stats"]   # action/state normalisation used at train time
```