Modelpublic

StarVLA/Qwen3-VL-GR00T-Robocasa-gr1

sourceHugging Faceupdated 23d agoView on Hugging Face

2likes98downloads

Model Card

StarVLA QwenGR00T Qwen3-VL-4B for RoboCasa GR1 (90K)

This repository contains the uploaded 90,000-step checkpoint from the 1226_fourier_gr1_unified_1000_QwenGR00T_qwen3 run. It is a state-conditioned StarVLA QwenGR00T policy trained on the 24-task Fourier GR1 RoboCasa mixture. Although the run configuration targets 100K and summary.jsonl lists a 100K step, the only published weight file is the 90K checkpoint.

Model details

Item	Published configuration
Framework	StarVLA `QwenGR00T`
VLM	Local snapshot named `Qwen3-VL-4B-Instruct-Action`; revision not recorded
Action model	16-layer `DiT-B` flow head: 768 latent width, 12 heads (64 dimensions/head); state/action decoder MLP width 1,024
Action dimension	29
Raw / model state dimension	29 / 58
Action chunk	16 steps
Inference flow steps	4
Camera	Single `video.ego_view`, 224 x 224
Dataset mixture	`fourier_gr1_unified_1000`
Normalization key	`gr1`
Uploaded checkpoint	`checkpoints/steps_90000_pytorch_model.pt`

The public GR1 data configuration orders state and action as left arm (7), right arm (7), left hand (6), right hand (6), and waist (3), for 29 raw dimensions. StateActionSinCosTransform expands the raw state to the model's configured 58D representation. Actions use per-key min-max normalization.

Training data

The mixture contains 24 GR1 tabletop pick-and-place tasks, with 1,000 trajectories named for each task in the public registry. The packaged statistics report 24,000 trajectories under the gr1 embodiment key.

Training details

Setting	Value in `config.yaml`
Intended maximum steps	100,000
Released step	90,000
Resume mode	`true`
Per-device VLA batch size	8
Gradient accumulation	1
Warm-up steps	5,000
Base / interface / action LR	`3e-5` / `1e-5` / `1e-4`
Optimizer fields	Betas `(0.9, 0.95)`, epsilon `1e-8`, weight decay `1e-8`; name not stored
Scheduler	Cosine with minimum LR `5e-7`
Diffusion repeats / inference steps	4 / 4
`freeze_modules`	Packaged boolean `true`; the public trainer expects module paths as a string, so this value names/selects no modules
Seed	42

No training hardware, global batch size, precision mode, or exact resume source is recorded in the Hub YAML, so those fields are not inferred here.

Related project-reported evaluation

The public RoboCasa GR1 evaluation guide links this repository for Qwen3VL-GR00T and reports the following result:

Reported model	Tasks	Rollouts per task	Average success rate
`StarVLA-GR00T-Qwen3`	24	50	47.8%

The published table names the model family but does not record a checkpoint step, SHA, or evaluation log bundle. Therefore 47.8% is a related project-reported result and is not presented as independently verified for the uploaded 90K artifact. Reproducible attribution requires the original rollout outputs or a checkpoint identifier from the evaluation run.

Files

text

config.yaml
dataset_statistics.json
summary.jsonl
checkpoints/
└── steps_90000_pytorch_model.pt

Keep the configuration and gr1 statistics with the checkpoint. The summary file enumerates saved training steps but contains no evaluation metrics.

Loading and evaluation

bash

huggingface-cli download StarVLA/Qwen3-VL-GR00T-Robocasa-gr1 \
  --local-dir Qwen3-VL-GR00T-Robocasa-gr1

export CKPT=$PWD/Qwen3-VL-GR00T-Robocasa-gr1/checkpoints/steps_90000_pytorch_model.pt
python deployment/model_server/server_policy.py \
  --ckpt_path "$CKPT" \
  --port 5678 \
  --use_bf16

Run the RoboCasa simulator in a separate environment following the evaluation guide linked above. Keep state input enabled for this GR00T checkpoint and use normalization key gr1. The --no_send_state note in that guide applies to the separate QwenOFT release, not this model.

Intended use and limitations

This checkpoint is intended for the 24-task RoboCasa GR1 tabletop simulation with the exact ego-camera, state/action ordering, and normalization contract above. The uploaded files do not prove the reported 47.8% result belongs to step 90K, and they do not document the resume source or VLM revision. Transfer to other GR1 stacks, camera layouts, robots, or real hardware is unverified. The policy is not safety-tuned.