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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -3,6 +3,161 @@ license: agpl-3.0
 task_categories:
 - robotics
 pretty_name: NYU Machines in Motion Lab
-size_categories:
-- 1B<n<10B
----
+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}
+}
+```
+
+
+
+
+
+
+

play/metadata.json

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+{
+  "num_shards": 13,
+  "episodes_per_shard": 1,
+  "total_episodes": 13,
+  "image_shape": [
+    256,
+    256,
+    3
+  ],
+  "action_shape": [
+    2
+  ],
+  "split": "success+failure"
+}

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