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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
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:
- Joint DOF (articulated body)
- Joint angles (radians)
- Joint angular velocities (rad/s)
- Root DOF (global free body)
- Root position (x, y, z)
- Root linear velocity (vx, vy, vz)
- 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 likeHumanoid
orGo2
, full root DOF and velocities are included.
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