StarVLA/StarVLA-Qwen3vl4b-PIv3-RoboDojo

StarVLA QwenPI_v3 Qwen3-VL-4B for RoboDojo (100k)

This directory contains one StarVLA QwenPI_v3 checkpoint initialized from Qwen3-VL-4B-Instruct and trained on the 35-task RoboDojo LeRobot v2.1 mixture. The VLM, VLM interface, and action model were trained end to end; this is not a LoRA or adapter-only checkpoint.

Model details

Item	Value
Framework	StarVLA QwenPI_v3
Base VLM	Qwen3-VL-4B-Instruct
Action model	36-layer LayerwiseFM
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
Inference flow steps	4
Checkpoint step	100,000
Checkpoint format	Complete StarVLA framework state dict (.pt)

The policy predicts a normalized 50 x 14 action chunk. RoboDojo evaluation must use the saved arx_x5 normalization statistics and execute 16 actions before requesting the next chunk.

Files

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 configuration and dataset statistics beside the checkpoints/ directory; StarVLA uses them to reconstruct the framework and unnormalize actions.

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. The bolded column is the policy in this directory.

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

This is a StarVLA checkpoint, not a Hugging Face from_pretrained() directory. Use the StarVLA model server and the XPolicyLab StarVLA adapter.

Start the model server from the StarVLA repository:

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:

STARVLA_CKPT_PATH="$CKPT" \
STARVLA_INCLUDE_STATE=True \
STARVLA_UNNORM_KEY=arx_x5 \
STARVLA_EXECUTE_HORIZON=16 \
bash eval.sh \
  RoboDojo build_tower qwenpi_v3_steps_100000 \
  arx_x5 joint 0 0 1 <policy_conda_env> <robodojo_conda_env>

The final arguments are the seed, policy GPU, simulator GPU, policy environment, and RoboDojo environment. Use the RoboDojo task registry's native episode counts when producing an official aggregate.

Intended use

This checkpoint is intended for RoboDojo simulation research with the ARX X5 dual-arm embodiment. Performance with different camera calibration, state/action ordering, normalization, robots, or real-world hardware has not been established.