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StarVLA/Qwen3VL-GR00T-Bridge-RT-1

sourceHugging Faceupdated 22d agoView on Hugging Face

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Model Card

Qwen3-VL-4B GR00T for Bridge + RT-1 (20K)

This repository contains a StarVLA QwenGR00T WidowX policy trained on the Bridge and Fractal/RT-1 LeRobot datasets. The action decoder is a diffusion transformer flow-matching head; this is not an OFT regression model.

Checkpoint identity

Item	Value
Released file	`checkpoints/steps_20000_pytorch_model.pt`
Training step	20,000
Hub revision checked	`12acc0b0f1f6230df21c479934a67a930b52f878`
File size	9,976,845,210 bytes
SHA-256 / LFS object ID	`769d6c400d582a86ae8df8b0b445240ab679dbe77eeb72a4db71e43cd129c7c3`

Although the configured run target is 100K and the summary contains later save events, no checkpoint later than 20K is published here.

Model and control contract

Item	Value
Framework	StarVLA `QwenGR00T`
Base VLM	Qwen3-VL-4B-Instruct
Action model	16-layer DiT-B flow head: 768 latent width, 12 heads (64 dimensions/head); state/action decoder MLP width 1,024
Observation	Language + one `image_0` RGB view at 224 x 224
Policy action / state dimensions	7 / 7; packaged dataset state statistics contain 8 values
Action chunk	`16 x 7`
Action representation	delta end-effector action (`delta_ee`)
Inference integration	4 flow steps
SimplerEnv normalization	`oxe_bridge` for WidowX Bridge evaluation
Action ensemble horizon	7

The SimplerEnv adapter sends image and language without robot state. Do not silently change the observation payload, action coordinate system, or normalization key. The YAML also carries a dinov2_vits14 field, but the matched QwenGR00T implementation does not construct a DINO or Q-Former module.

Training data and settings

The bridge_rt_1 mixture combines the packaged oxe_bridge statistics (1,284,318 transitions, 53,192 trajectories) and oxe_rt1 statistics (3,449,678 transitions, 87,212 trajectories).

Setting	Value
Per-device VLA batch size	16
Gradient accumulation	1
Training flow repeats	4; the public run-era framework reads trainer value `4`, not the YAML action-model value `8`
Optimizer	AdamW, betas `(0.9, 0.95)`, epsilon `1e-8`, weight decay `1e-8`
Warmup	10,000 steps
VLM co-training loss scale	0.1
`freeze_modules`	Packaged boolean `true`; the public trainer expects module paths as a string, so this value names/selects no modules
Seed	42
Training GPU count	Missing from the public artifact

Packaged-log evaluation

The repository packages 16 logs whose filenames and final command lines point to steps_20000_pytorch_model.pt: four SimplerEnv Bridge tasks, four runs per task, and 24 episodes per run (384 episodes total).

Task	Success
Put carrot on plate	59.38%
Put eggplant in basket	100.00%
Put spoon on tablecloth	75.00%
Stack green cube on yellow cube	18.75%
Micro-average, 384 episodes	63.28%

These values are recomputed from the packaged logs. The StarVLA model-zoo page lists 65.3%, but that number is not reproduced by this artifact's 16 logs and is therefore not used as the checkpoint score here.

Download and load

bash

hf download StarVLA/Qwen3VL-GR00T-Bridge-RT-1 \
  --local-dir playground/Pretrained_models/Qwen3VL-GR00T-Bridge-RT-1

export CKPT=playground/Pretrained_models/Qwen3VL-GR00T-Bridge-RT-1/checkpoints/steps_20000_pytorch_model.pt
python deployment/model_server/server_policy.py \
  --ckpt_path "$CKPT" \
  --config_override framework.qwenvl.base_vlm=Qwen/Qwen3-VL-4B-Instruct \
  --port 57700 \
  --use_bf16

Evaluate through the WidowX Bridge adapter documented in examples/simBenchmarks/SimplerEnv/README.md, using oxe_bridge, delta-EEF actions, and the same ensemble settings.

Intended use and limitations

The policy is intended for the four WidowX Bridge tasks in SimplerEnv. Its results do not establish performance on RT-1 robot hardware, other embodiments, or real-world deployment. Preserve the matching StarVLA framework and treat the pickle-based checkpoint as executable data from a trusted revision.

License status

This target repository did not previously publish a Model Card or a separate LICENSE file. The checkpoint's weight license therefore needs maintainer confirmation; the Qwen3-VL base-model and training-dataset terms still apply.