---
language: en
license: mit
tags:
  - robotics
  - openvla
  - openvla-oft
  - simplevla-rl
  - robotwin
  - sft
  - aloha
  - vla
pipeline_tag: robotics
---

# SFT-Robo2: beat_block_hammer

OpenVLA-OFT SFT checkpoint for the **beat_block_hammer** task from RoboTwin 2.0, trained to match the SimpleVLA-RL paper (arXiv:2509.09674) settings.

## Model Details

- **Base model:** [openvla/openvla-7b](https://huggingface.co/openvla/openvla-7b)
- **Fine-tuning method:** LoRA (rank 32) on LLaMA2-7B backbone
- **Training objective:** Cross-entropy on discrete action tokens (NOT L1 regression)
- **Architecture:** Single-view image + proprioception + language instruction -> 25 action chunks x 14D (bimanual ALOHA)

## Training Config

| Parameter | Value |
|-----------|-------|
| max_steps | 10,000 |
| batch_size | 8 per GPU x 2 GPUs = 16 global |
| learning_rate | 5e-4 |
| lora_rank | 32 |
| num_images_in_input | 1 (head camera only) |
| use_l1_regression | False |
| use_film | False |
| use_proprio | True |
| use_diffusion | False |
| image_aug | True |
| NUM_ACTIONS_CHUNK | 25 |
| ACTION_DIM | 14 |
| ACTION_PROPRIO_NORMALIZATION_TYPE | bounds |

## Training Data

- **1000 expert demonstrations** from RoboTwin 2.0 beat_block_hammer task
- Collected with curobo motion planner under full domain randomization
- 950 train / 50 val split (5% validation)
- Expert success rate: ~70% (filtered to successful trajectories only)

## Evaluation Results

| Metric | Value |
|--------|-------|
| **Success rate (seed 0, 100 episodes)** | **45.0%** |
| Success rate (seed 1, partial 16 episodes) | 50.0% |
| Paper SFT baseline (Table 4) | 28.1% |
| Prior Phase 1 reproduction | 35.2% |

Evaluated with greedy sampling (do_sample=False), 100 held-out scenarios, demo_randomized config.

## Compatibility

This checkpoint is compatible with:
- **SimpleVLA-RL** RL training script (run_openvla_oft_rl_twin2.sh)
- **RoboTwin 2.0** evaluation suite (policy/openvla-oft/eval.sh)

**Important:** This checkpoint uses cross-entropy discrete action tokens (LLaMA2 head), NOT L1 regression with MLP action head. Ensure your inference code passes use_l1_regression=False and passes proprioceptive state to predict_action.

## Training Infrastructure

- **Hardware:** 2x NVIDIA H200 (141GB each)
- **Training time:** 5.4 hours
- **Platform:** UNSW Katana HPC (PBSPro scheduler)

## Citation

If you use this checkpoint, please cite the SimpleVLA-RL paper: arXiv:2509.09674

## License

MIT