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---
dataset_name: MoSim Dataset
language:
- en
license: mit
tags:
- reinforcement-learning
- world-model
- dynamics-simulation
- motion
- state-action
- npz
task_categories:
- reinforcement-learning
pretty_name: MoSim Dataset
size_categories:
- 1K<n<10K
---
# 🗂️ 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 contains sequential **state-action trajectories** for training and evaluating **MoSim (Neural Motion Simulator)** world models.
All trajectories are collected **from random policies** in classical control and locomotion environments.
---
## 📦 Dataset Overview
- **Format**: `.npz` (NumPy compressed arrays)
- **Contents**:
- `*_random.npz`: training episodes
- `*_random_test.npz`: test episodes
- **Episode length**: 1000 steps per episode
---
## 📊 Data Structure
Each `.npz` file contains:
| Key | Shape | Description |
|------------|-------------------------------------|------------------------------------|
| `states` | *(num_episodes, 1000, state_dim)* | State at each timestep |
| `actions` | *(num_episodes, 1000, action_dim)* | Action applied at each timestep |
**State composition**:
1. **Joint DOF (articulated body)**
- Joint angles *(radians)*
- Joint angular velocities *(rad/s)*
2. **Root DOF (global free body)**
- Root position *(x, y, z)*
- Root linear velocity *(vx, vy, vz)*
3. **Root Orientation & Rotation**
- Root rotation quaternion *(qx, qy, qz, qw)*
- Root angular velocity *(wx, wy, wz)*
> ⚡ For manipulation control tasks like `Panda`, only joint angles and velocities are provided.
> ⚡ For locomotion tasks like `Humanoid` or `Go2`, full root DOF and velocities are included.
---
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