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README.md

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+# 🗂️ MoSim Dataset
+
+Official release of the dataset from the paper:  
+**[Neural Motion Simulator: Pushing the Limit of World Models in Reinforcement Learning](https://arxiv.org/pdf/2504.07095)**  
+
+This dataset provides the training and evaluation data used for the **MoSim (Neural Motion Simulator)** world models.  
+All trajectories are collected **exclusively using random policies** across multiple standard simulation environments.  
+
+---
+
+## 📦 Dataset Overview
+
+- **Format**: Parquet files for efficient loading and processing.  
+- **Contents**:  
+  - State sequences (`qpos`, `qvel`, sensor readings)  
+  - Action sequences (raw control inputs)  
+  - Environment metadata (episode length, environment name)  
+- **Size**: ~XX GB (after compression)  
+- **Environments**: Includes standard MuJoCo-based tasks such as `Cheetah`, `Hopper`, `Humanoid`, and `Go2`.  
+
+---
+
+## 📊 Structure
+
+Each trajectory is stored as a sequence of steps:
+
+| Field          | Shape (per step)       | Description                               |
+|----------------|-----------------------|-------------------------------------------|
+| `qpos`         | *(n_joints,)*         | Joint positions                            |
+| `qvel`         | *(n_joints,)*         | Joint velocities                           |
+| `action`       | *(action_dim,)*       | Environment action                         |
+| `reward`       | *1*                   | Immediate reward                           |
+| `done`         | *1*                   | Episode termination flag                   |
+| `env_id`       | *string*              | Environment identifier                     |
+
+All episodes are segmented and zero-padded to fixed length for training convenience.
+
+---
+
+## 📥 Usage
+
+```python
+from datasets import load_dataset
+
+# Load the full MoSim dataset
+dataset = load_dataset("wujiss1/MoSim_Dataset")
+
+# Access one environment subset
+hopper_data = dataset["train"].filter(lambda x: x["env_id"] == "Hopper")
+print(hopper_data[0].keys())