---
license: apache-2.0
base_model: Qwen/Qwen3-VL-4B-Instruct
tags:
  - robotics
  - vision-language-action
  - starvla
  - qwen3-vl
  - robodojo
---

# StarVLA QwenOFT Qwen3-VL-4B for RoboDojo (100k)

This directory contains one StarVLA `QwenOFT` checkpoint initialized from
Qwen3-VL-4B-Instruct and trained on the 35-task RoboDojo LeRobot v2.1 mixture.
The VLM, VLM interface, and MLP action head were trained end to end.

## Model details

| Item | Value |
| --- | --- |
| Framework | StarVLA `QwenOFT` |
| Base VLM | Qwen3-VL-4B-Instruct |
| Action model | MLP, hidden size 2,560 |
| Objective | L1 action regression |
| Action representation | 14D absolute joint position (`abs_qpos`) |
| Action horizon | 50 |
| State dimension | 14 |
| Camera input | Head, left wrist, right wrist; resized to 224 x 224 |
| Checkpoint step | 100,000 |
| Checkpoint format | Complete StarVLA framework state dict (`.pt`) |

## Files

```text
README.md
config.yaml
config.full.yaml
dataset_statistics.json
summary.jsonl
checkpoints/
└── steps_100000_pytorch_model.pt
```

Only the requested 100k checkpoint is included. Keep the saved configuration and
normalization statistics beside the checkpoint.

## Training details

| Setting | Value |
| --- | --- |
| Dataset mixture | `robodojo_v21_all_h50_q99` |
| Training tasks | 35 |
| Per-GPU batch size | 16 |
| Gradient accumulation | 1 |
| Frozen modules | None |
| Optimizer | AdamW, betas `(0.9, 0.95)`, epsilon `1e-8` |
| VLM learning rate | `1e-5` |
| VLM-interface learning rate | `1e-5` |
| Action-model learning rate | `1e-4` |
| Schedule | Cosine, 5,000 warmup steps, minimum LR `5e-7` |
| Gradient checkpointing | Enabled |
| Random seed | 42 |

## Official RoboDojo evaluation

All policies below use the official complete 42-task protocol: 50 episodes per
task, 2,100 episodes per policy. Values are shown as `SR (%) / Score`. This
directory's policy is bolded. Higher is better for both SR and Score.

### Group summary

| Policy | Average | Generalization | Precision | Long-Horizon | Memory | Open |
| --- | ---: | ---: | ---: | ---: | ---: | ---: |
| **QwenOFT** | **4.86 / 8.01** | **4.33 / 6.42** | **11.75 / 17.54** | **5.50 / 12.95** | **1.67 / 1.77** | **0.50 / 0.60** |
| QwenGR00T | 3.81 / 7.35 | 3.50 / 6.52 | 5.75 / 10.09 | 6.50 / 15.46 | 3.33 / 4.37 | 0.00 / 0.00 |
| QwenPI_v3 | 6.19 / 9.60 | 4.17 / 7.28 | 14.00 / 19.06 | 10.00 / 17.84 | 2.00 / 2.32 | 0.75 / 0.88 |

### Task details

Task values are `SR (%) / Score`; each task uses 50 episodes.

| Evaluation group / task | **QwenOFT** | QwenGR00T | QwenPI_v3 |
| --- | ---: | ---: | ---: |
| **Generalization** | **4.33 / 6.42** | 3.50 / 6.52 | 4.17 / 7.28 |
| stack_bowls | **18.00 / 21.00** | 10.00 / 14.80 | 14.00 / 16.70 |
| push_T | **0.00 / 0.00** | 0.00 / 0.00 | 0.00 / 0.00 |
| pack_objects_into_box | **0.00 / 3.10** | 0.00 / 7.80 | 0.00 / 6.80 |
| fold_clothes | **10.00 / 12.80** | 8.00 / 12.40 | 2.00 / 9.60 |
| hang_mugs | **0.00 / 3.60** | 0.00 / 3.00 | 0.00 / 3.50 |
| sweep_blocks | **0.00 / 0.00** | 0.00 / 0.00 | 2.00 / 2.00 |
| pour_liquid_into_cup | **14.00 / 14.00** | 14.00 / 14.00 | 12.00 / 12.00 |
| make_toast | **0.00 / 1.00** | 2.00 / 5.00 | 2.00 / 5.00 |
| arrange_largest_number | **0.00 / 1.90** | 2.00 / 4.10 | 2.00 / 5.70 |
| sort_nesting_dolls_by_size | **0.00 / 0.00** | 4.00 / 4.00 | 6.00 / 6.00 |
| store_laptop_and_headphones | **4.00 / 11.20** | 0.00 / 7.20 | 2.00 / 8.40 |
| stack_blocks | **6.00 / 8.40** | 2.00 / 5.90 | 8.00 / 11.60 |
| **Precision** | **11.75 / 17.54** | 5.75 / 10.09 | 14.00 / 19.06 |
| fasten_screws | **4.00 / 8.00** | 0.00 / 2.00 | 0.00 / 6.00 |
| plug_in_charger | **6.00 / 6.00** | 2.00 / 2.00 | 4.00 / 4.00 |
| insert_tubes | **40.00 / 51.60** | 28.00 / 40.40 | 44.00 / 56.80 |
| pour_balls_into_vase | **8.00 / 8.00** | 0.00 / 0.00 | 2.00 / 2.00 |
| play_Xylophone | **0.00 / 0.00** | 0.00 / 0.00 | 0.00 / 0.00 |
| deposit_coin | **0.00 / 3.20** | 2.00 / 5.60 | 6.00 / 7.60 |
| insert_key | **0.00 / 12.90** | 0.00 / 9.90 | 0.00 / 11.10 |
| build_tower | **36.00 / 50.60** | 14.00 / 20.80 | 56.00 / 65.00 |
| **Long-Horizon** | **5.50 / 12.95** | 6.50 / 15.46 | 10.00 / 17.84 |
| put_bottles_into_dustbin | **22.00 / 40.90** | 26.00 / 44.40 | 64.00 / 73.60 |
| fill_pen_holder | **4.00 / 11.70** | 6.00 / 14.40 | 8.00 / 23.00 |
| classify_objects | **2.00 / 5.50** | 6.00 / 11.50 | 0.00 / 7.50 |
| play_tic_tac_toe | **0.00 / 12.40** | 2.00 / 16.40 | 0.00 / 6.80 |
| fill_egg_holder | **0.00 / 0.60** | 0.00 / 0.00 | 0.00 / 0.80 |
| organize_table | **0.00 / 16.50** | 0.00 / 25.00 | 4.00 / 27.00 |
| make_kong | **16.00 / 16.00** | 12.00 / 12.00 | 4.00 / 4.00 |
| play_stacking_toy | **0.00 / 0.00** | 0.00 / 0.00 | 0.00 / 0.00 |
| **Memory** | **1.67 / 1.77** | 3.33 / 4.37 | 2.00 / 2.32 |
| cover_blocks | **0.00 / 0.60** | 6.00 / 12.10 | 0.00 / 1.50 |
| match_and_pick_from_conveyor | **10.00 / 10.00** | 14.00 / 14.00 | 12.00 / 12.00 |
| swap_blocks | **0.00 / 0.00** | 0.00 / 0.00 | 0.00 / 0.00 |
| swap_T | **0.00 / 0.00** | 0.00 / 0.00 | 0.00 / 0.00 |
| press_by_number | **0.00 / 0.00** | 0.00 / 0.00 | 0.00 / 0.00 |
| imitate_sorting_sequence | **0.00 / 0.00** | 0.00 / 0.10 | 0.00 / 0.40 |
| **Open** | **0.50 / 0.60** | 0.00 / 0.00 | 0.75 / 0.88 |
| align_blocks | **0.00 / 0.00** | 0.00 / 0.00 | 0.00 / 0.00 |
| general_pickup | **4.00 / 4.00** | 0.00 / 0.00 | 6.00 / 6.00 |
| stack_blocks_by_language | **0.00 / 0.80** | 0.00 / 0.00 | 0.00 / 0.80 |
| solve_equation | **0.00 / 0.00** | 0.00 / 0.00 | 0.00 / 0.00 |
| classify_objects_by_language | **0.00 / 0.00** | 0.00 / 0.00 | 0.00 / 0.20 |
| pick_from_conveyor_by_image | **0.00 / 0.00** | 0.00 / 0.00 | 0.00 / 0.00 |
| store_tools_in_toolbox | **0.00 / 0.00** | 0.00 / 0.00 | 0.00 / 0.00 |
| pour_by_language | **0.00 / 0.00** | 0.00 / 0.00 | 0.00 / 0.00 |

## Evaluation

Start the StarVLA model server:

```bash
export CKPT=/path/to/MODEL_DIR/checkpoints/steps_100000_pytorch_model.pt
python deployment/model_server/server_policy.py \
  --ckpt_path "$CKPT" \
  --port 57700 \
  --use_bf16
```

Run a RoboDojo task from `XPolicyLab/policy/starVLA`:

```bash
STARVLA_CKPT_PATH="$CKPT" \
STARVLA_INCLUDE_STATE=True \
STARVLA_UNNORM_KEY=arx_x5 \
STARVLA_EXECUTE_HORIZON=16 \
bash eval.sh \
  RoboDojo build_tower qwenoft_steps_100000 \
  arx_x5 joint 0 0 1 <policy_conda_env> <robodojo_conda_env>
```

Use RoboDojo `scripts/internal/summarize_result.py` for official aggregation.

## Intended use

This checkpoint is intended for RoboDojo simulation research with the ARX X5
dual-arm embodiment. Performance outside the saved observation, action, and
normalization contract has not been established.

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