--- title: InternW0-RoboTwin-clean canonical_url: "https://www.modelscope.cn/models/InternRobotics/InternW0-RoboTwin-clean" md_url: "https://www.modelscope.cn/models/InternRobotics/InternW0-RoboTwin-clean.md" repository: InternRobotics/InternW0-RoboTwin-clean last_updated: 2026-10-23 license: mit pipeline_tag: robotics tasks: - robotics model_type: - internw0 base_model: - InternRobotics/InternW0-Base base_model_relation: finetune library_name: - pytorch frameworks: - pytorch language: - en downloads: 0 stars: 0 tags: - robotics - world-model - robot-learning - manipulation - flow-matching - video-action-model - robotwin - InternW0 --- # InternW0-RoboTwin-clean > InternW0-RoboTwin-clean - InternRobotics 在 ModelScope 开源的模型。InternW0-RoboTwin-clean InternRobotics/InternW0-RoboTwin-clean 是 ModelScope 魔搭社区上的robotics模型，采用 mit 许可，基于 InternRobotics/InternW0-Base 构建。 - **Repository**: InternRobotics/InternW0-RoboTwin-clean - **License**: mit - **Tasks**: robotics - **Base model**: InternRobotics/InternW0-Base - **Tags**: robotics, world-model, robot-learning, manipulation, flow-matching, video-action-model, robotwin, InternW0 - **Downloads**: 0 - **Stars**: 0 - **Last updated**: 2026-10-23 Source: https://www.modelscope.cn/models/InternRobotics/InternW0-RoboTwin-clean --- # InternW0-RoboTwin-clean [![Paper](https://img.shields.io/badge/Paper-arXiv%202609.27656-b31b1b)](https://arxiv.org/abs/2609.27656) [![Project](https://img.shields.io/badge/Project-InternW0-2f80ed)](https://internrobotics.github.io/internw0) [![Code](https://img.shields.io/badge/Code-GitHub-181717?logo=github)](https://github.com/InternRobotics/InternW0) [![Base model](https://img.shields.io/badge/Base-InternW0--Base-6b7280)](https://huggingface.co/InternRobotics/InternW0-Base) [![Downloads last month](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fhuggingface.co%2Fapi%2Fmodels%2FInternRobotics%2FInternW0-RoboTwin-clean&query=%24.downloads&label=downloads%20last%20month&logo=huggingface)](https://huggingface.co/InternRobotics/InternW0-RoboTwin-clean) ## Model description **InternW0-RoboTwin-clean** is the task-posttrained InternW0 checkpoint for the RoboTwin 2.0 **Clean2Random** setting. It is initialized from the heterogeneous [InternW0-Base](https://huggingface.co/InternRobotics/InternW0-Base) pretraining checkpoint and posttrained only on clean demonstrations. The selected checkpoint is then evaluated on clean scenes and unseen randomized scenes. InternW0 couples a slow Wan2.2-TI2V-5B VideoDiT planner with a fast ActionDiT executor. Observation-conditioned chunk K/V routing adapts cached future-video context to each newly observed state, allowing the model to reuse long-horizon predictions while generating responsive action chunks. This is a **native `w0` checkpoint**, not a standalone Transformers model. It is intended for RoboTwin evaluation or compatible continued posttraining. ## Files and integrity | File | Purpose | Size | SHA-256 | |---|---|---:|---| | `InternW0-RoboTwin-clean.pt` | Canonical model-only native checkpoint | 14,705,453,767 bytes | `b718314f57c2febb159deb03d92cf9ebba830f7c75920c0b6e34c4903b91f5a2` | | `pytorch_model.bin` | Zero-copy LFS alias used for PyTorch download statistics | Same LFS object | `b718314f57c2febb159deb03d92cf9ebba830f7c75920c0b6e34c4903b91f5a2` | | `config.json` | Machine-readable model/download-counting manifest | 1,702 bytes | `82d8a7a6f25fe14720dc7d6e02136fa02fff0f0b2650a29866eacc4edf275347` | | `checkpoint_metadata.json` | Embedded behavior/training/statistics/provenance export | 16,176 bytes | `6e4b67d29ca9a369c870f510e9304ffb3e4aec974f6e38a09c35d598c4fbaf54` | | `normalization.json` | Matching RoboTwin action/proprio statistics | 4,014 bytes | `31362a24460803c5080ef925b807acbc7af3e08d847c9db222bc2a33c0d96653` | | `posttrain_config.yaml` | Portable public-code posttraining config | 1,661 bytes | `109ca7e501c65e4072060ec7477da2a5486f45aed095ece661714ef92f477f88` | | `evaluation_config.yaml` | Portable public-code evaluation config | 885 bytes | `89545dbed7a002c3a1e09edf48a3540d30d977e96b89e6fad64ab0b92020fd38` | | `source_training_config.yaml` | Historical resolved config with portable paths | 6,175 bytes | `6455e1dda379528fe76d5c85e4161bce651286897567bd55ebe2c45c7e9e4312` | | `source_evaluation_config.yaml` | Historical evaluation reference with portable paths | 7,312 bytes | `6b16dc8583a79d69a253f1b3a367aab072c8dcf24737ece784c9a926d807aff3` | | `paper_results.json` | Paper-reported Clean2Random result record | 621 bytes | `9396065aded7413f6d4ce0228ac512bb3c97fa0b32157fb8b15f19c1e82ef2cf` | | `PROVENANCE.md` | Selection, lineage, training, and evaluation provenance | 2,446 bytes | `0cca6d854f5bf6ee3dccd84128584bdae398f9f02e053c016d9d097bd944680a` | | `LICENSE` | MIT license | 1,205 bytes | `b1d3d0160bdd9748bfc4e8acb9ab8fbce7ab725c827fd6fc673261f57aae0306` | `pytorch_model.bin` is a second Git-LFS path to the exact same object as `InternW0-RoboTwin-clean.pt`. It does not duplicate the 14.7 GB payload. ## Checkpoint summary - Selected step: `10545` - Serialization dtype: `bfloat16` - Model-only checkpoint: yes - Optimizer state: excluded - Tensors: 1,977 - Parameter elements: 7,352,528,643 - Source heterogeneous pretraining step: `30000` - Target domain: `robotwin_wam/aloha` - Target soft prompts: K=8 - Canonical action/proprio dimensionality: 37 - Valid ALOHA joint/gripper slots: 14 - Action horizon: 64 - Action chunk size: 16 - Video plan horizon: 96 action ticks - Video/action stride: 8 - History frames: 0 - Action semantics: native absolute joint command at interval end The checkpoint embeds a schema-v4 `behavior_config`, `training_recipe`, `posttrain_statistics`, and `posttrain_provenance`. The public loader performs a semantic contract check before loading weights. ## RoboTwin Clean2Random training The retained recipe uses: - clean demonstrations only; - native RoboTwin joint-space data; - 25 Hz sampling (`sampling_stride=2` from 50 Hz); - every valid native window; - ColorJitter with brightness 0.2, contrast 0.2, saturation 0.2, and hue 0; - action offset 0; - batch size 12 per process; - 15 epochs; - learning rate 3e-5 with cosine decay; - seed 42. The matching normalization statistics are in `normalization.json`. The historical run resumed from step 5,000 of the preceding clean run and selected step 10,545 from the continued best Clean2Random run. See `PROVENANCE.md` for the full lineage. ## Evaluation result The paper-reported RoboTwin 2.0 Clean2Random results are: | Split | Success rate | |---|---:| | Clean | **83.20%** | | Randomized, unseen during training | **68.00%** | | Average | **75.60%** | The source evaluation reference uses 50 tasks, 15 episodes per task, 10 inference steps, a 64-action horizon, four action chunks per video prefill, and a fixed-period 25 Hz controller. The historical resolved protocol is preserved in `source_evaluation_config.yaml`. ## Required external assets This checkpoint is not self-contained. Users must separately obtain and comply with the licenses for: ```bash hf download Wan-AI/Wan2.2-TI2V-5B --local-dir assets/wan22 hf download facebook/dinov3-vitb16-pretrain-lvd1689m \ --local-dir assets/dinov3-vitb16-pretrain-lvd1689m ``` A RoboTwin checkout, the matching RoboTwin data export, and its UMT5 text cache are also required. The repository does not bundle datasets, simulation code, Wan2.2 files, or DINOv3 weights. ## Quick start ### 1. Install the public codebase ```bash git clone https://github.com/InternRobotics/InternW0.git cd InternW0 conda create -n w0 python=3.10 -y conda activate w0 pip install -U pip setuptools wheel pip install torch==2.7.1 torchvision==0.22.1 \ --extra-index-url https://download.pytorch.org/whl/cu128 pip install -e . ``` ### 2. Download this checkpoint ```bash hf download InternRobotics/InternW0-RoboTwin-clean \ config.json pytorch_model.bin checkpoint_metadata.json \ normalization.json posttrain_config.yaml evaluation_config.yaml \ source_training_config.yaml source_evaluation_config.yaml \ paper_results.json PROVENANCE.md LICENSE \ --local-dir assets/robotwin mv assets/robotwin/pytorch_model.bin \ assets/robotwin/InternW0-RoboTwin-clean.pt ``` ### 3. Run RoboTwin evaluation ```bash export W0_WAN_MODEL_DIR="$PWD/assets/wan22" export W0_DINO_WEIGHTS="$PWD/assets/dinov3-vitb16-pretrain-lvd1689m" export W0_ROBOTWIN_DATA="$PWD/data/robotwin" export W0_ROBOTWIN_STATS="$PWD/assets/robotwin/normalization.json" export W0_ROBOTWIN_CHECKPOINT="$PWD/assets/robotwin/InternW0-RoboTwin-clean.pt" export ROBOTWIN_ROOT="$PWD/external/RoboTwin" python experiments/robotwin/eval_robotwin_single.py \ EVALUATION.robotwin_root="$ROBOTWIN_ROOT" \ EVALUATION.task_name= \ EVALUATION.ckpt="$W0_ROBOTWIN_CHECKPOINT" ``` For compatible continued posttraining: ```bash bash scripts/train_zero1.sh 8 --config-name posttrain_robotwin ``` ## Download statistics Hugging Face uses library-specific query files for model download statistics. For the `pytorch` library, the Hub counts HTTP `GET` and `HEAD` requests to `pytorch_model.bin`. This repository provides that path as a zero-copy alias of the canonical checkpoint. The badge above reads the public Hub API `downloads` field, corresponding to downloads in the last 30 days. Hub analytics are aggregated and may lag briefly. ## Intended uses - RoboTwin 2.0 Clean2Random reproduction and extension. - Research on world-action models, context routing, and cross-embodiment robot learning. - Compatible continued posttraining with the public `w0` codebase. ## Limitations - This is a simulation-domain research checkpoint, not a deployed robot controller. - It requires the public InternW0 codebase, external Wan2.2 and DINOv3 assets, RoboTwin data, text caches, and substantial compute. - It is not directly compatible with `transformers.AutoModel`. - Real-robot use requires independent safety analysis, constrained workspaces, and human oversight. ## License The weights and metadata in this repository are released under the [MIT License](./LICENSE). Third-party assets obtained separately remain subject to their original licenses. ## Citation If you use InternW0, please cite: ```bibtex @article{internw0, title = {InternW0: A Foundational Physical World Model for Efficient Real-World Interactions}, author = {Jisong Cai and Yao Mu and Ganlin Yang and Zhe Cao and Zhangzheng Tu and Xing Gao and Kailin Li and Xinyu Zhan and Lixin Yang and Yangkun Zhu and Haoxiang Ma and Ming Zhou and Qiaojun Yu and Yufei Xue and Liqun He and Yifei Yao and Yifan Zhu and Long Ling and Bingqi Jiang and Haoyu Guo and Xueyue Zhu and Bowen Zhou and Bin Zhao and Tianfan Xue and Chunhua Shen and Weinan Zhang}, journal = {arXiv preprint arXiv:2609.27656}, year = {2026} } ```