Update README.md

README.md

@@ -18,7 +18,7 @@ tags:
  RDT-1B is a 1B-parameter imitation learning Diffusion Transformer pre-trained on 1M+ multi-robot episodes. 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, pos. to vel., and even with a mobile chassis.
 
- All the [code](https://github.com/GeneralEmbodiedSystem/RoboticsDiffusionTransformer/tree/main?tab=readme-ov-file) and pre-trained model weights are licensed under the MIT license.
+ All the [code](https://github.com/GeneralEmbodiedSystem/RoboticsDiffusionTransformer/tree/main?tab=readme-ov-file), pre-trained model weights, and [data](https://github.com/thu-ml/RoboticsDiffusionTransformer) are licensed under the MIT license.
 
  Please refer to our [project page](https://rdt-robotics.github.io/rdt-robotics/) and [paper]() for more information.
 
@@ -39,18 +39,23 @@ tags:
 
  ## Uses
 
- RDT takes language instruction, RGB image (of up to three views), control frequency (if any), and proprioception as input and predicts the next 64 robot actions in the form of the unified action space vector.
-The unified action space vector includes all the main physical quantities of the robot manipulator (e.g., the end-effector and joint, position and velocity, and base movement). 
-To deploy on your robot platform, you need to pick the relevant quantities from the unified vector. See our repository for more information.
+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 base movement). 
+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 for a tutorial.
 
- Here's an example of how to use the RDT-1B model for inference on a robot:
+Here's an example of how to use the RDT-1B model for inference on a robot:
 ```python
-# Clone the repository and install dependencies
+# 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 = {
@@ -68,8 +73,9 @@ model = create_model(
     pretrained='robotics-diffusion-transformer/rdt-1b',
     control_frequency=25,
 )
+
 # Start inference process
-# Load pre-computed language embeddings
+# Load the pre-computed language embeddings
 lang_embeddings_path = 'your/language/embedding/path' 
 text_embedding = torch.load(lang_embeddings_path)['embeddings']  
 images: List(PIL.Image) = ... #  The images from last 2 frame
@@ -82,7 +88,7 @@ actions = policy.step(
 )
 ```
 
- <!-- RDT-1B supports finetuning on custom datasets, deploying and inferencing on real robots, as well as retraining the model.
+ <!-- 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. -->