JianuoCao/TEXEDO-Checkpoint

TEXEDO — Checkpoints

Test-Time Scaling for Controller-Aware Language-Conditioned Humanoid Motion Generation

This repository hosts the pretrained checkpoints and runtime assets for TEXEDO, a text-to-motion pipeline for the Unitree G1 humanoid. Given a language prompt, TEXEDO generates multiple candidate motions, decodes them into a 36-dimensional G1 robot motion format, scores them with dynamic and semantic verifiers, and selects the best candidate for deployment.

• 🌐 Project page: https://jianuocao.github.io/TEXEDO/
• 💻 Code: https://github.com/JianuoCao/TEXEDO
• 📄 Paper: https://arxiv.org/abs/2606.22998
• 📦 Dataset: https://huggingface.co/datasets/JianuoCao/TEXEDO

Contents

Logical name	What it is	Approx. size
fsq_tokenizer	FSQ motion tokenizer (encoder/decoder + codebook) for 36-dim G1 motion	~216 MB
fsq_norm_stats	Per-channel normalization stats for the tokenizer	~2 KB
generator	Stage-2 text→motion generator: flan-t5-base fine-tuned on FSQ motion tokens (multi-task)	~3.2 GB
dynamic_verifier	Dynamic-feasibility (physical-plausibility) scorer	~40 MB
dynamic_norm_stats	Normalization stats paired with the dynamic verifier	~2 KB
semantic_evaluator	Text–motion matching evaluator (match net + decomposition + meta)	variable
glove	GloVe vocab for the semantic text encoder	~20 MB
g1_robot	Unitree G1 MuJoCo model (XML + meshes)	~26 MB

The base LM google/flan-t5-base is loaded from the public Hub at runtime and is not re-hosted here.

Usage

The checkpoints are designed to be fetched automatically by the TEXEDO code:

git clone https://github.com/JianuoCao/TEXEDO.git
cd TEXEDO
conda env create -f environment.yml
conda activate TEXEDO
pip install -e .

# Downloads these checkpoints + runtime assets into ./assets
python scripts/download_assets.py

Then run the full generate → score → select → render pipeline:

python -m pipeline.generate --prompt "a person waves with the right hand" --num-samples 8 --out-dir candidates/
python -m pipeline.score   --motion-dir candidates/ --caption "a person waves with the right hand" --output scores.csv
python -m pipeline.select_best_of_n --scores scores.csv --motion-dir candidates/ --copy-best-to best/
python scripts/visualize_csv.py --input-dir best/ --output-dir viz/

You can also download a single file directly:

from huggingface_hub import hf_hub_download

ckpt = hf_hub_download(
    repo_id="JianuoCao/TEXEDO-Checkpoint",
    filename="tokenizer/checkpoint_epoch_95.pt",
)

See the repo's docs/MODELS.md for the full asset manifest and layout.

Citation

@misc{cao2026texedotesttime,
  title={TEXEDO: Test-Time Scaling for Controller-Aware Language-Conditioned Humanoid Motion Generation},
  author={Jianuo Cao and Yuxin Chen and Yuzhen Song and Masayoshi Tomizuka and Chenran Li and Thomas Tian},
  year={2026},
  eprint={2606.22998},
  archivePrefix={arXiv},
  primaryClass={cs.RO},
  url={https://arxiv.org/abs/2606.22998},
}

License

Released under the MIT license. Third-party datasets, pretrained base models, robot assets, and dependencies retain their own licenses and terms of use.