Update README.md

README.md

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 ---
+license: mit
+language:
+- en
+pipeline_tag: robotics
+library_name: transformers
 tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
+- robotics
+- pytorch
+- multimodal
+- pretraining
+- vla
+- diffusion
+- rdt
 ---
+# RDT-170M
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: https://huggingface.co/robotics-diffusion-transformer/rdt-1b
-- Docs: [More Information Needed]
+![](head.mp4)
+ RDT-170M is a 170M-parameter imitation learning Diffusion Transformer ***(RDT(small) in ablation)***. Given language instruction and RGB images of up to three views, RDT can predict the next 
+64 robot actions. RDT is compatible with almost all modern mobile manipulators, from single-arm to dual-arm, joint to EEF, position to velocity, and even wheeled locomotion.
+
+ All the [code](https://github.com/thu-ml/RoboticsDiffusionTransformer/tree/main?tab=readme-ov-file), pre-trained model weights, and [data](https://huggingface.co/datasets/robotics-diffusion-transformer/rdt-ft-data) are licensed under the MIT license.
+
+ Please refer to our [project page](https://rdt-robotics.github.io/rdt-robotics/) and [paper](https://arxiv.org/pdf/2410.07864) for more information.
+
+ ## Model Details
+
+ - **Developed by:** The RDT team consisting of researchers from the [TSAIL group](https://ml.cs.tsinghua.edu.cn/) at Tsinghua University
+- **Task Type:** Vision-Language-Action (language, image => robot actions)
+- **Modle Type:** Diffusion Policy with Transformers
+- **License:** MIT
+- **Language(s) (NLP):** en
+- **Multi-Modal Encoders:**
+  - **Vision Backbone:** [siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384)
+  - **Language Model:** [t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl)
+- **Pre-Training Datasets:** 46 datasets consisting of [RT-1 Dataset](https://robotics-transformer1.github.io/), [RH20T](https://rh20t.github.io/), [DROID](https://droid-dataset.github.io/), [BridgeData V2](https://rail-berkeley.github.io/bridgedata/), [RoboSet](https://robopen.github.io/roboset/), and a subset of [Open X-Embodiment](https://robotics-transformer-x.github.io/). See [this link](https://github.com/thu-ml/RoboticsDiffusionTransformer/blob/main/docs/pretrain.md#download-and-prepare-datasets) for a detailed list.
+- **Repository:** https://github.com/thu-ml/RoboticsDiffusionTransformer
+- **Paper :** https://arxiv.org/pdf/2410.07864
+- **Project Page:** https://rdt-robotics.github.io/rdt-robotics/
+
+ ## Uses
+
+RDT takes language instruction, RGB images (of up to three views), control frequency (if any), and proprioception as input and predicts the next 64 robot actions.
+RDT supports control of almost all robot manipulators with the help of the unified action space, which 
+includes all the main physical quantities of the robot manipulator (e.g., the end-effector and joint, position and velocity, and the wheeled locomotion). 
+To deploy on your robot platform, you need to fill the relevant quantities of the raw action vector into the unified space vector. See [our repository](https://github.com/thu-ml/RoboticsDiffusionTransformer) for more information.
+
+ **Out-of-Scope**: Due to the embodiment gap, RDT cannot yet generalize to new robot platforms (not seen in the pre-training datasets). 
+In this case, we recommend collecting a small dataset of the target robot and then using it to fine-tune RDT.
+See [our repository](https://github.com/thu-ml/RoboticsDiffusionTransformer) for a tutorial.
+
+Here's an example of how to use the RDT-1B model for inference on a robot:
+```python
+# Please first clone the repository and install dependencies
+# Then switch to the root directory of the repository by "cd RoboticsDiffusionTransformer"
+
+# Import a create function from the code base
+from scripts.agilex_model import create_model
+
+# Names of cameras used for visual input
+CAMERA_NAMES = ['cam_high', 'cam_right_wrist', 'cam_left_wrist']
+config = {
+    'episode_len': 1000,  # Max length of one episode
+    'state_dim': 14,      # Dimension of the robot's state
+    'chunk_size': 64,     # Number of actions to predict in one step
+    'camera_names': CAMERA_NAMES,
+}
+pretrained_vision_encoder_name_or_path = "google/siglip-so400m-patch14-384" 
+# Create the model with the specified configuration
+model = create_model(
+    args=config,
+    dtype=torch.bfloat16, 
+    pretrained_vision_encoder_name_or_path=pretrained_vision_encoder_name_or_path,
+    pretrained='robotics-diffusion-transformer/rdt-1b',
+    control_frequency=25,
+)
+
+# Start inference process
+# Load the pre-computed language embeddings
+# Refer to scripts/encode_lang.py for how to encode the language instruction
+lang_embeddings_path = 'your/language/embedding/path'
+text_embedding = torch.load(lang_embeddings_path)['embeddings']  
+images: List(PIL.Image) = ... #  The images from last 2 frames
+proprio = ... # The current robot state
+# Perform inference to predict the next `chunk_size` actions
+actions = policy.step(
+    proprio=proprio,
+    images=images,
+    text_embeds=text_embedding 
+)
+```
+
+ <!-- RDT-1B supports finetuning on custom datasets, deploying and inferencing on real robots, and retraining the model.
+Please refer to [our repository](https://github.com/GeneralEmbodiedSystem/RoboticsDiffusionTransformer/blob/main/docs/pretrain.md) for all the above guides. -->
+
+
+## Citation
+
+If you find our work helpful, please cite us:
+```bibtex
+@article{liu2024rdt,
+  title={RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation},
+  author={Liu, Songming and Wu, Lingxuan and Li, Bangguo and Tan, Hengkai and Chen, Huayu and Wang, Zhengyi and Xu, Ke and Su, Hang and Zhu, Jun},
+  journal={arXiv preprint arXiv:2410.07864},
+  year={2024}
+}
+```
+Thank you!