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README.md

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+---
+license: mit
+task_categories:
+  - robotics
+tags:
+  - robotics
+  - manipulation
+  - franka
+  - visuomotor
+  - isaac-sim
+  - isaac-lab
+  - mimicgen
+  - cosmos
+  - block-stacking
+  - imitation-learning
+size_categories:
+  - n<1K
+---
+
+# Block Stacking MimicGen Dataset
+
+## Description
+
+202 demonstrations of a Franka Emika Panda robot stacking colored blocks into a bowl.
+Generated using NVIDIA MimicGen from source teleoperation demonstrations and augmented with Cosmos world foundation models.
+
+## Task
+
+**Objective:** Pick up 3 colored cubes (Jenga-style blocks) and place them into a bowl on the table.
+
+**Success Criteria:** All 3 blocks must be placed inside the bowl.
+
+## Robot Platform
+
+- **Robot:** Franka Emika Panda (7-DoF manipulator)
+- **Gripper:** Franka Hand (parallel jaw)
+- **Simulator:** NVIDIA Isaac Lab (Isaac Sim)
+- **Control Mode:** Joint velocity control
+- **Control Frequency:** 30 Hz
+
+## Data Modalities
+
+| Modality | Key | Shape | Type | Description |
+|----------|-----|-------|------|-------------|
+| Table Camera RGB | `obs/table_cam` | (T, 200, 200, 3) | uint8 | Third-person view |
+| Wrist Camera RGB | `obs/wrist_cam` | (T, 200, 200, 3) | uint8 | Eye-in-hand view |
+| Table Camera Depth | `obs/table_cam_depth` | (T, 200, 200, 1) | float32 | Depth map |
+| Surface Normals | `obs/table_cam_normals` | (T, 200, 200, 3) | float32 | Normal vectors |
+| Segmentation | `obs/table_cam_segmentation` | (T, 200, 200, 4) | uint8 | Instance segmentation |
+| End-Effector Position | `obs/eef_pos` | (T, 3) | float32 | XYZ position |
+| End-Effector Orientation | `obs/eef_quat` | (T, 4) | float32 | Quaternion (x,y,z,w) |
+| Joint Positions | `obs/joint_pos` | (T, 9) | float32 | 7 arm + 2 gripper |
+| Joint Velocities | `obs/joint_vel` | (T, 9) | float32 | 7 arm + 2 gripper |
+| Gripper Position | `obs/gripper_pos` | (T, 2) | float32 | Finger positions |
+| Cube Positions | `obs/cube_positions` | (T, 9) | float32 | 3 cubes x XYZ |
+| Cube Orientations | `obs/cube_orientations` | (T, 12) | float32 | 3 cubes x quaternion |
+| Actions | `actions` | (T, 7) | float32 | Joint velocity commands |
+
+## Dataset Statistics
+
+- **Total Demonstrations:** 202
+- **Average Episode Length:** ~515 steps
+- **Total Frames:** ~104,000
+- **File Size:** 16 GB
+
+## File Format
+
+HDF5 file with the following structure:
+
+```
+cosmos_generated_202.hdf5
+└── data/
+    ├── demo_0/
+    │   ├── actions
+    │   ├── obs/
+    │   │   ├── table_cam
+    │   │   ├── wrist_cam
+    │   │   ├── eef_pos
+    │   │   └── ...
+    │   ├── states/
+    │   └── initial_state/
+    ├── demo_1/
+    └── ...
+```
+
+## Usage
+
+### Python (h5py)
+
+```python
+import h5py
+import numpy as np
+
+# Load dataset
+with h5py.File('cosmos_generated_202.hdf5', 'r') as f:
+    demos = list(f['data'].keys())
+    print(f"Number of demos: {len(demos)}")
+
+    # Access a single demo
+    demo = f['data/demo_0']
+    actions = demo['actions'][:]
+    images = demo['obs/table_cam'][:]
+    eef_pos = demo['obs/eef_pos'][:]
+
+    print(f"Episode length: {len(actions)}")
+    print(f"Image shape: {images.shape}")
+```
+
+### PyTorch DataLoader
+
+```python
+import h5py
+import torch
+from torch.utils.data import Dataset, DataLoader
+
+class BlockStackingDataset(Dataset):
+    def __init__(self, hdf5_path):
+        self.f = h5py.File(hdf5_path, 'r')
+        self.demos = list(self.f['data'].keys())
+
+    def __len__(self):
+        return len(self.demos)
+
+    def __getitem__(self, idx):
+        demo = self.f[f'data/{self.demos[idx]}']
+        return {
+            'images': torch.from_numpy(demo['obs/table_cam'][:]),
+            'actions': torch.from_numpy(demo['actions'][:]),
+            'eef_pos': torch.from_numpy(demo['obs/eef_pos'][:])
+        }
+
+dataset = BlockStackingDataset('cosmos_generated_202.hdf5')
+loader = DataLoader(dataset, batch_size=1, shuffle=True)
+```
+
+## Generation Pipeline
+
+1. **Source Demonstrations:** Human teleoperation via VR hand tracking
+2. **MimicGen Augmentation:** Automated trajectory generation with randomized object poses
+3. **Cosmos Enhancement:** Visual augmentation using NVIDIA Cosmos world foundation models
+4. **Quality Filtering:** 202/714 trajectories passed success criteria (28.3% success rate)
+
+## Compatible Frameworks
+
+This dataset can be converted for use with:
+
+- **LeRobot** (HuggingFace) - Convert to Parquet + MP4
+- **OpenVLA** - Convert to RLDS TFRecord
+- **Pi0/OpenPI** (Physical Intelligence) - LeRobot v2 format
+- **GROOT N1** (NVIDIA) - LeRobot v2 with 224x224 images
+- **Cosmos Transfer** (NVIDIA) - MP4 videos + JSON annotations
+
+See `DATASET_FORMAT_GUIDE.md` for conversion instructions.
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@misc{block_stacking_mimicgen_2026,
+  title={Block Stacking MimicGen Dataset},
+  author={Tshiamo},
+  year={2026},
+  publisher={HuggingFace},
+  url={https://huggingface.co/datasets/tshiamor/block-stacking-mimic}
+}
+```
+
+## License
+
+MIT License
+
+## Acknowledgments
+
+- NVIDIA Isaac Lab and MimicGen teams
+- NVIDIA Cosmos for visual augmentation
+- HuggingFace for dataset hosting