---
license: mit
---


# MaNI Series
```
The MaNI series is a real-world simulation dataset for robotic manipulation, focusing on complex
human-object interaction and fine-grained motion modeling. This dataset systematically covers various manipulation
types common in real-world robotic operation scenarios, including grasping, pushing, pulling, rotating, assembly,
and tool use. It models real-world contact, friction, constraints, and dynamics through high-fidelity physical simulation.
MaNI employs a multi-modal data acquisition and synchronization mechanism, including RGB/Depth visual observations,
6-DoF poses of objects and end-effectors, joint states, force/torque feedback, and continuous motion trajectories,
providing rich and stable data support for learning end-to-end manipulation strategies from perception to control.
Unlike purely simulated or purely real-world datasets, MaNI reduces the sim-to-real gap by reconstructing real-world
scenarios and calibrating parameters in the simulation environment. This dataset is particularly suitable for research
directions such as imitation learning, reinforcement learning, and manipulation planning, and can serve as an important
benchmark for robotic skill learning, generalization ability evaluation, and complex interaction reasoning.
```

MODAL : Data Confuser "Small files only, Big files confusion"

CHARS : 517253f76137x0p

DCVERSION : 0.0.1.2 built 260210