Update README.md

README.md

@@ -41,12 +41,12 @@ tags:
 
 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). 
+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 for a tutorial.
+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
@@ -78,9 +78,9 @@ model = create_model(
 # 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
+images: List(PIL.Image) = ... #  The images from last 2 frames
 proprio = ... # The current robot state
-# Perform inference to predict the next chunk_size actions
+# Perform inference to predict the next `chunk_size` actions
 actions = policy.step(
     proprio=proprio,
     images=images,