Upload TurboVLA LIBERO and RoboTwin checkpoints

CHECKSUMS.sha256

@@ -0,0 +1,5 @@
+d0183df6bafd44507b6c797da5c5ab080ef8446cde4a8127d7280546d9f7c034  checkpoints/robotwin/steps_55000_ema_model.safetensors
+75cabe8b67394300e9714c5b55c740c5c45a105880e00d6192873722915bddd9  checkpoints/libero/object.pth
+fa9f9a19b2f3aa8e9342a19b1d45ebf09f0b7fecd9e9460354d4cf4833184b4b  checkpoints/libero/goal.pth
+ad59cdbbe27ac7f4efc2209abf7e83be519303cfdaf4e94082533815beeec4a2  checkpoints/libero/spatial.pth
+a6153155981a03a7d63522e8ac881e73e9edfeebe183df375d8cc84432ed95b0  checkpoints/libero/long.pth

DINOv3_LICENSE.md

@@ -0,0 +1,66 @@
+# DINOv3 License
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LICENSE

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README.md

@@ -1,3 +1,145 @@
 ---
-license: apache-2.0
+datasets:
+- StarVLA/RoboTwin-Clean
+language:
+- en
+license: other
+license_name: dinov3-license
+license_link: https://huggingface.co/H-EmbodVis/TurboVLA/blob/main/DINOv3_LICENSE.md
+pipeline_tag: robotics
+tags:
+- robotics
+- embodied-ai
+- vision-language-action
+- robot-manipulation
+- pytorch
+- safetensors
+- dinov3
+- libero
+- robotwin
+arxiv: "2607.27205"
 ---
+
+<h1 align="center">TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with &lt;1 GB VRAM</h1>
+
+<div align="center">
+  <a href="https://arxiv.org/abs/2607.27205"><img src="https://img.shields.io/badge/arXiv-Paper-b31b1b?logo=arxiv" alt="Paper"></a>
+  <a href="https://h-embodvis.github.io/TurboVLA/"><img src="https://img.shields.io/badge/Homepage-Project-orange.svg?logo=googlehome" alt="Project page"></a>
+  <a href="https://github.com/H-EmbodVis/TurboVLA"><img src="https://img.shields.io/badge/GitHub-Repository-green?logo=github" alt="GitHub repository"></a>
+  <a href="https://huggingface.co/H-EmbodVis/TurboVLA/blob/main/DINOv3_LICENSE.md"><img src="https://img.shields.io/badge/Weights-DINOv3%20License-blue" alt="DINOv3 License"></a>
+
+  <h5><em>Hengyi Xie<sup>1*</sup>, Chenfei Yao<sup>1*</sup>, Xianjin Wu<sup>1</sup>, Xuanyang Xi<sup>2</sup>, Yiping Tang<sup>2</sup>, Di Xu<sup>2</sup>, Yingying Zhu<sup>1</sup>, Dingkang Liang<sup>1&dagger;</sup>, Xiang Bai<sup>1</sup>, Han Ding<sup>1</sup></em></h5>
+
+  <sup>1</sup> Huazhong University of Science and Technology, China<br>
+  <sup>2</sup> Huawei Technologies Co. Ltd, China<br>
+  <sup>*</sup> Equal contribution, listed alphabetically by surname. <sup>&dagger;</sup> Project lead.
+</div>
+
+## 🔍 Overview
+
+**TurboVLA** is a real-time vision-language-action model for robotic manipulation. It replaces the conventional LLM-centric vision-to-language-to-action pathway with a direct vision-plus-language-to-action design: visual observations and language instructions are encoded independently, exchanged through lightweight bidirectional interaction, and decoded into continuous action chunks.
+
+This repository contains the official TurboVLA checkpoints for the **LIBERO** and **RoboTwin 2.0** evaluations.
+
+<details>
+  <summary><b>Abstract</b></summary>
+
+Vision-language-action (VLA) models commonly adopt an LLM-centric V → L → A pathway, where visual observations are projected into the representation space of a large language model before being decoded into robot actions. Although effective, this design incurs substantial computation and memory overhead at every policy invocation. In this work, we introduce TurboVLA, a new VLA paradigm that reformulates the conventional V → L → A pathway as a direct V + L → A mapping. Instead of using a large language model as the central interface between perception and action, TurboVLA independently encodes visual observations and language instructions, directly exchanges information between them through lightweight bidirectional vision-language interaction, and predicts continuous action chunks with a compact decoder. This simple design constructs task-conditioned representations directly from visual and linguistic features, significantly reducing the computational and memory costs of VLA inference. On LIBERO, TurboVLA achieves 97.7% average success with only 0.2B parameters, 31.2 ms inference latency, and 0.9 GB inference VRAM on a consumer-grade RTX 4090, matching or outperforming substantially larger VLA policies. These results establish TurboVLA as a simple and effective alternative to the prevailing LLM-centric VLA paradigm, offering a new perspective on how vision, language, and action can be connected for efficient robotic manipulation.
+</details>
+
+### ✨ Key Ideas
+
+- Direct vision-plus-language-to-action modeling without an LLM-centric action pathway.
+- Independent DINOv3 visual and BERT language encoders with lightweight bidirectional interaction.
+- Compact action-chunk decoders for low-latency and memory-efficient inference.
+- Official checkpoints for both LIBERO and RoboTwin 2.0.
+
+## 🎯 Task, Inputs, and Outputs
+
+TurboVLA predicts continuous robot action chunks from synchronized multi-view RGB observations, a natural-language instruction, and the robot proprioceptive state.
+
+| Benchmark | Inputs | Output | Action horizon | Visual encoder |
+| --- | --- | --- | ---: | --- |
+| LIBERO | Two RGB views, language instruction, robot state | 7-D continuous actions | 12 | DINOv3 ViT-B |
+| RoboTwin 2.0 | Three 224 x 224 RGB views, language instruction, 14-D robot state | 14-D absolute joint-position actions | 50 | DINOv3 ViT-L |
+
+## 📦 Released Checkpoints
+
+### LIBERO
+
+The LIBERO model was jointly trained on all four suites in a single mixed-suite training run. The released evaluation uses the following suite-specific checkpoints:
+
+| Suite | Evaluation name | Checkpoint |
+| --- | --- | --- |
+| LIBERO-Object | `libero_object` | `checkpoints/libero/object.pth` |
+| LIBERO-Goal | `libero_goal` | `checkpoints/libero/goal.pth` |
+| LIBERO-Spatial | `libero_spatial` | `checkpoints/libero/spatial.pth` |
+| LIBERO-Long | `libero_10` | `checkpoints/libero/long.pth` |
+
+The LIBERO files are model-state-only PyTorch checkpoints. Optimizer and scheduler states were removed; the released `model_state_dict` tensors are unchanged and are compatible with the official TurboVLA loader.
+
+### RoboTwin 2.0
+
+| Training data | Weights | Checkpoint |
+| --- | --- | --- |
+| RoboTwin Clean, 50 tasks | EMA | `checkpoints/robotwin/` |
+
+## 🛠️ Usage
+
+You can download the complete model repository using either the Hugging Face CLI or the Python SDK.
+
+**Option 1: Hugging Face CLI (Recommended)**
+
+```bash
+pip install -U huggingface_hub
+hf download H-EmbodVis/TurboVLA --local-dir TurboVLA-model
+```
+
+**Option 2: Python SDK**
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="H-EmbodVis/TurboVLA",
+    repo_type="model",
+    local_dir="TurboVLA-model",
+)
+```
+
+TurboVLA is a custom PyTorch VLA implementation and is not directly loadable with `transformers.AutoModel.from_pretrained()`. For installation and evaluation instructions, use the [official GitHub repository](https://github.com/H-EmbodVis/TurboVLA).
+
+## 📂 Repository Contents
+
+| Path | Description |
+| --- | --- |
+| `checkpoints/libero/*.pth` | Four LIBERO suite-specific evaluation checkpoints |
+| `checkpoints/robotwin/*.safetensors` | RoboTwin 2.0 EMA checkpoint |
+| `libero_all4_stats.json` | Mixed-suite LIBERO normalization statistics |
+| `config.yaml` | RoboTwin model and inference configuration |
+| `dataset_statistics.json` | RoboTwin state and action normalization statistics |
+| `config.json` | Hugging Face model metadata and checkpoint index |
+| `CHECKSUMS.sha256` | SHA-256 checksums for all released checkpoints |
+| `DINOv3_LICENSE.md` | DINOv3 terms applicable to the released checkpoint parameters |
+| `LICENSE` | Apache-2.0 license for the TurboVLA project code |
+
+## ⚖️ License
+
+The TurboVLA source code is released under Apache-2.0. The released checkpoints contain parameters derived from DINOv3 and are distributed subject to the included [DINOv3 License](DINOv3_LICENSE.md). Users must comply with all applicable third-party license terms.
+
+## 📖 Citation
+
+If you find this work useful, please consider citing:
+
+```bibtex
+@article{xie2026turbovla,
+  title  = {TurboVLA: Real-Time Vision-Language-Action Model at
+            32 Hz on an RTX 4090 with <1 GB VRAM},
+  author = {Xie, Hengyi and Yao, Chenfei and Wu, Xianjin and
+            Xi, Xuanyang and Tang, Yiping and Xu, Di and
+            Zhu, Yingying and Liang, Dingkang and Bai, Xiang and
+            Ding, Han},
+  journal = {arXiv preprint arXiv:2607.27205},
+  year   = {2026}
+}
+```

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config.json

@@ -0,0 +1,35 @@
+{
+  "model_type": "turbovla",
+  "architectures": [
+    "GroundingDINOVLA",
+    "Grounding_DINO_DiT"
+  ],
+  "framework": "pytorch",
+  "task": "robotics",
+  "benchmarks": [
+    "LIBERO",
+    "RoboTwin 2.0"
+  ],
+  "checkpoint_formats": [
+    "pytorch",
+    "safetensors"
+  ],
+  "released_checkpoints": {
+    "libero_object": {
+      "path": "checkpoints/libero/object.pth"
+    },
+    "libero_goal": {
+      "path": "checkpoints/libero/goal.pth"
+    },
+    "libero_spatial": {
+      "path": "checkpoints/libero/spatial.pth"
+    },
+    "libero_10": {
+      "path": "checkpoints/libero/long.pth"
+    },
+    "robotwin": {
+      "ema": true,
+      "path": "checkpoints/robotwin/steps_55000_ema_model.safetensors"
+    }
+  }
+}

config.yaml

@@ -0,0 +1,93 @@
+datasets:
+  vla_data:
+    action_mode: abs
+    data_mix: robotwin_clean_50
+    data_root_dir: ./playground/Datasets/RoboTwin
+    dataset_py: lerobot_datasets
+    per_device_batch_size: 48
+    sequential_step_sampling: false
+    video_backend: pyav
+framework:
+  action_model:
+    act_dim_feedforward: 2048
+    act_dropout: 0.1
+    act_hidden_dim: 256
+    act_loss_type: l1
+    act_mlp_hidden_dim: 512
+    act_nheads: 8
+    act_num_layers: 3
+    act_state_hidden_dim: 256
+    act_state_tokens: 2
+    action_dim: 14
+    action_hidden_dim: 1024
+    action_horizon: 50
+    action_model_type: act
+    diffusion_model_cfg:
+      cross_attention_dim: 256
+    hidden_size: 1024
+    state_dim: 14
+  dinov3:
+    attn_implementation: flash_attention_2
+    freeze_vision_encoder: false
+    image_size: 224
+    local_files_only: false
+    model_path: facebook/dinov3-vitl16-pretrain-lvd1689m
+    num_views: 3
+    vision_dropout: 0.1
+    vision_pos_init_std: 0.01
+    vision_pos_scale_init: 0.01
+  fusion:
+    enhancer_inner_dim: 1024
+    fusion_dropout: 0.0
+    fusion_droppath: 0.1
+    hidden_dim: 256
+    nheads: 8
+    num_layers: 6
+    text_dropout: 0.0
+  groundingdino:
+    load_bert: false
+    load_feature_enhancer: false
+    load_pretrained: false
+    load_text_proj: false
+    pretrained_ckpt: null
+  name: GroundingDINODiT
+  text:
+    attn_implementation: flash_attention_2
+    bert_path: google-bert/bert-base-uncased
+    freeze_text_encoder: true
+    local_files_only: false
+    max_text_len: 256
+    sub_sentence_present: true
+output_dir: .
+run_id: turbovla_robotwin_clean50_55k_ema
+run_root_dir: .
+seed: 42
+trainer:
+  ema_decay: 0.999
+  ema_device: cuda
+  eval_interval: 1000
+  freeze_modules: text_encoder.bert
+  gradient_clipping: 1.0
+  learning_rate:
+    action_model: 5.0e-05
+    base: 5.0e-05
+    dinov3: 5.0e-05
+    feature_enhancer: 5.0e-05
+    text_encoder: 5.0e-05
+    vision_proj: 5.0e-05
+  logging_frequency: 50
+  lr_scheduler_type: warmup_constant_with_factor
+  max_train_steps: 100000
+  num_warmup_steps: 1000
+  optimizer:
+    betas:
+    - 0.9
+    - 0.95
+    eps: 1.0e-08
+    weight_decay: 1.0e-10
+  save_format: safetensors
+  save_interval: 5000
+  scheduler_specific_kwargs:
+    warmup_start_factor: 0.0
+wandb_entity: null
+wandb_project: TurboVLA-RoboTwin

dataset_statistics.json

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libero_all4_stats.json

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