---
license: agpl-3.0
task_categories:
- robotics
pretty_name: NYU Machines in Motion Lab
tags:
- robot-learning
- manipulation
- imitation-learning
- offline-rl
- world-models
- dreamer
---

# MiM Flexiv PushT Dataset 

A real-robot dataset collected on a Flexiv Rizon 10S consisting of ~4 hours of (1) random play interactions and (2) push-to-goal episodes, with RGB observations at 5 Hz and 2D delta-position actions.

<p align="center">
  <img src="assets/play-rollout.gif" width="35%" />
</p>

## What’s inside

### Tasks / subsets
This repository contains two related subsets:

- **Play (random interaction):**
  - Unstructured episodes with no explicit start/end goals.
  - Intended for representation learning, world modeling, and offline RL from diverse behavior.

- **Push-to-goal (task):**
  - Each episode pushes a T-shaped object toward the **center of the board**.
  - Intended for imitation learning, offline RL, and goal-conditioned learning (if you add goals).

### Observation and action spaces
- **Observation (`images`)**
  - RGB image at **5 Hz**
  - Stored as `uint8` in the range `[0, 255]`
  - Resolution: `256 x 256 x 3`
  - Tensor shape per episode in HDF5: `1 x seq_len x 256 x 256 x 3`

- **Action (`actions`)**
  - 2D delta position command in the board/camera plane: `(delta_x, delta_y)`
  - Stored as `float32`
  - Tensor shape per episode in HDF5: `1 x seq_len x 2`

### Dataset scale (approx.)
- Total recording time: ~4 hours
- Frame rate: 5 Hz
- Approx. total frames: ~72,000 (4 * 3600 * 5)

> Note: Exact counts per split are provided in the metadata JSON shipped with each subset.

## Data format and files

### File layout 
```
.
├── README.md
├── assets/
│ └── preview.gif
├── play/
│ ├── metadata.json
│ ├── shard_000.hdf5
│ ├── shard_001.hdf5
│ └── ...
└── task/
├── metadata.json
├── shard_000.hdf5
├── shard_001.hdf5
└── ...
```

### HDF5 schema
Each HDF5 file contains two main datasets:
- `images`: shape `1 x seq_len x 256 x 256 x 3`, dtype `uint8`
- `actions`: shape `1 x seq_len x 2`, dtype `float32`

### Metadata JSON schema
Each subset includes a `metadata.json` describing global stats and shapes. Example:
```json
{
  "num_shards": 13,
  "episodes_per_shard": 1,
  "total_episodes": 13,
  "image_shape":,[256,256,3]
  "action_shape":,[2]
  "min_action": ,[min_1, min_2]
  "max_action": ,[max_1, max_2]
}
```

## How to use
### Quickstart (Python)
Below is a minimal example for loading one episode with h5py:
```python
import h5py
import numpy as np

path = "play/shard_000.hdf5"  # or "task/shard_000.hdf5"
with h5py.File(path, "r") as f:
    images = f["images"]   # (seq_len, 256, 256, 3), uint8
    actions = f["actions"] # (seq_len, 2), float32

# Example: normalize images to  float[4]
images_f = images.astype(np.float32) / 255.0
```

## DreamerV4 world model (simulator)
A DreamerV4 world model has been trained on this dataset to enable “simulated” policy training/evaluation inside a learned dynamics model.

World model link: [Here](https://github.com/machines-in-motion/dreamer-v4/tree/release)

**Notes:**

- The world model is provided for research convenience and may not perfectly capture real-world contacts/friction.

- Please report issues / inconsistencies via GitHub issues or the Hugging Face discussion tab.


## Data collection details

- Platform: Flexiv Rizon 10S (real robot) in-lab setup

- Camera: Orbec Femto Bolt fixed view looking at the board with the T object

- Control/action logged: 2D delta position (x, y) per timestep

- Observation logged: RGB image at 5 Hz

- Play subset: random exploration / interaction

- Task subset: pushing T to board center


## Limitations
- Actions are 2D deltas; they do not include full robot state, forces, or contact measurements.

- Camera viewpoint and lab conditions may limit generalization.

## License
This dataset is released under AGPL-3.0.

## Citation
If you use this dataset, please cite:

```
text
@dataset{mim_pusht_2025,
  title = {MiM Flexiv PushT Dataset},
  author = {Rooholla Khorrambakht},
  year = {2025},
  url = {https://huggingface.co/datasets/Rooholla/MiM-PushT}
}
```