README.md

metadata

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

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.

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📦 Dataset Overview

• Format: .npz (NumPy compressed arrays)
• Contents:
  • *_random.npz: training episodes
  • *_random_test.npz: test episodes
• Episode length: 1000 steps per episode

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📊 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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