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StarVLA/
Qwen3-VL-OFT-RoboTwin2-All

Name: Qwen3-VL-OFT-RoboTwin2-All
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Qwen3-VL OFT baseline for RoboTwin 2.0

This repository provides a baseline policy for RoboTwin 2.0 trained using a minimal visual-to-action configuration.

The goal of this release is to provide a simple and reproducible baseline for RoboTwin, using:

RGB observations only
direct absolute joint prediction
no robot state input
no action transformer
no pretraining

Despite its simplicity, the model achieves strong performance across RoboTwin 2.0 tasks.

Model and action contract

Component	Setting
VLM	Qwen3-VL-4B
Action head	Two-block residual MLP, 2,560 input / 5,120 hidden / 14 output; direct L1 regression

The policy directly maps RGB observations (head + two wrist cameras) to 14D absolute joint positions. The saved config uses a 50-step action horizon and does not enable robot-state input.

Item	Value
StarVLA framework	`QwenOFT`
Base VLM	Qwen3-VL-4B-Instruct
Action/state dimensions	14 / no state input recorded
Action mode	Absolute joint position
Action horizon	50
Input resolution	224 × 224
Released checkpoint	`checkpoints/steps_140000_pytorch_model.pt`
Checkpoint format	Complete StarVLA framework state dict (`.pt`)

Training recipe

Setting	Value
Dataset mixture	`robotwin_all_50`
Configured maximum steps	150,000
Released step	140,000
Per-device batch size	4
Gradient accumulation	1
Warm-up	5,000 steps
Base / VLM-interface / action LR	`1e-5 / 1e-5 / 1e-4`
`freeze_modules`	Packaged boolean `true`; the public trainer expects module paths as a string, so this value names/selects no modules
Seed	42

Training dataset

The model is trained using the official RoboTwin 2.0 dataset.

The training data consists of demonstrations from 50 manipulation tasks, combining clean demonstrations and randomized demonstrations.

Clean Demonstrations

50 tasks × 50 trajectories per task

Randomized Demonstrations

50 tasks × 500 trajectories per task

The randomized demonstrations include environment perturbations to improve robustness.

Evaluation protocol

The model is evaluated on RoboTwin 2.0 using 50 manipulation tasks.

Each task is evaluated under two evaluation settings:

Easy setting
Hard setting

For each task and setting:

100 evaluation trials are performed
The success rate is reported

Final results are computed as the average success rate across tasks.

Benchmark results

Task	Easy	Hard
stack_blocks_two	1.0000	1.0000
place_cans_plasticbox	1.0000	0.9900
grab_roller	1.0000	1.0000
place_empty_cup	1.0000	1.0000
shake_bottle_horizontally	1.0000	1.0000
lift_pot	1.0000	1.0000
place_burger_fries	1.0000	1.0000
move_playingcard_away	1.0000	0.9800
adjust_bottle	1.0000	0.9900
shake_bottle	1.0000	1.0000
blocks_ranking_rgb	0.9900	0.9800
stack_bowls_two	0.9900	1.0000
place_container_plate	0.9900	0.9900
press_stapler	0.9900	0.9600
place_object_stand	0.9900	0.9800
open_laptop	0.9800	1.0000
handover_mic	0.9800	0.9600
move_pillbottle_pad	0.9800	1.0000
handover_block	0.9700	0.9300
place_shoe	0.9600	1.0000
stack_bowls_three	0.9500	0.9100
place_fan	0.9400	0.9500
scan_object	0.9400	0.9100
stack_blocks_three	0.9400	0.8600
place_object_basket	0.9300	0.9400
beat_block_hammer	0.9300	0.9200
place_object_scale	0.9300	0.9300
place_dual_shoes	0.9100	0.8900
pick_dual_bottles	0.9100	0.9300
place_bread_basket	0.9100	0.7800
dump_bin_bigbin	0.9100	0.9400
move_can_pot	0.9100	0.9000
put_bottles_dustbin	0.9000	0.8500
place_a2b_left	0.9000	0.9500
place_bread_skillet	0.8900	0.8000
put_object_cabinet	0.8900	0.9100
place_a2b_right	0.8800	0.9500
rotate_qrcode	0.8800	0.9000
pick_diverse_bottles	0.8700	0.8600
place_mouse_pad	0.8700	0.9400
stamp_seal	0.8600	0.9000
place_phone_stand	0.8600	0.9500
blocks_ranking_size	0.7900	0.8000
place_can_basket	0.7500	0.7500
move_stapler_pad	0.7400	0.9000
turn_switch	0.6500	0.6200
click_alarmclock	0.5800	0.5100
hanging_mug	0.3400	0.2900
open_microwave	0.2800	0.3900
click_bell	0.2300	0.2700
Average	0.8818	0.8832

Download and evaluation

bash

huggingface-cli download StarVLA/Qwen3-VL-OFT-RoboTwin2-All \
  --local-dir Qwen3-VL-OFT-RoboTwin2-All

CKPT="$PWD/Qwen3-VL-OFT-RoboTwin2-All/checkpoints/steps_140000_pytorch_model.pt"
CUDA_VISIBLE_DEVICES=0 python deployment/model_server/server_policy.py \
  --ckpt_path "$CKPT" --port 5694 --use_bf16 \
  --config_override framework.qwenvl.base_vlm=Qwen/Qwen3-VL-4B-Instruct

The RoboTwin evaluator must use the saved absolute-action convention, normalization statistics, camera order, and 50-step prediction contract. See the StarVLA RoboTwin guide.

Evidence and limitations

Architecture and training settings were checked against config.yaml; the released 140k step was checked against the Hub file tree.
The benchmark table and 100-trial-per-task protocol are retained from the original Card. This repository does not include raw rollout logs, so the aggregate cannot be independently reconstructed from the Hub files alone.
Only the 140k weight is available; the 150k configured endpoint is not released here.
The policy is tied to the saved three-camera, absolute-joint and normalization contract. Different camera ordering, action mapping, chunk execution horizon, or normalization can materially change results.
Results cover RoboTwin 2.0 simulation only. Real-robot performance and safety are not established.

Citation

If you use this model or benchmark results in your research, please cite RoboTwin and this repository.

bibtex

@article{community2026starvla,
  title         = {StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing},
  author        = {Community, StarVLA},
  journal       = {arXiv preprint arXiv:2604.05014},
  year          = {2026},
  eprint        = {2604.05014},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO}
}