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README.md

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+---
+license: mit
+task_categories:
+  - robotics
+tags:
+  - robotics
+  - manipulation
+  - force-torque
+  - imitation-learning
+  - flow-matching
+  - zarr
+pretty_name: ForceFlow Dataset
+size_categories:
+  - 10G<n<100G
+---
+
+# ForceFlow Dataset
+
+**ForceFlow: Learning to Feel and Act via Contact-Driven Flow Matching**
+
+[[Project Page](https://jokeresc.github.io/ForceFlow-page)] | [[Code](https://github.com/JokerESC/ForceFlow)]
+
+![motivation](https://huggingface.co/datasets/JokerESC/ForceFlow/resolve/main/motivation-1.png)
+
+This dataset accompanies the ForceFlow framework — a force-aware reactive policy for contact-rich robot manipulation. It contains 7 real-robot demonstration tasks collected on a UFACTORY xArm6 with a 6-axis F/T sensor and dual Intel RealSense cameras.
+
+---
+
+## Tasks
+
+| Task | Episodes | Total Steps | Description |
+|---|---|---|---|
+| `plug` | 100 | 50,107 | Insert an electrical plug into a socket |
+| `stamp` | 100 | 45,867 | Press a rubber stamp onto a surface |
+| `clean_whiteboard` | 100 | 56,810 | Wipe a whiteboard with an eraser |
+| `clean_vase` | 50 | 85,478 | Wipe the surface of a vase |
+| `peel` | 50 | 38,564 | Peel adhesive tape from a surface |
+| `insert` | 50 | 25,032 | Insert a peg into a hole |
+| `press_button` | 50 | 23,396 | Press a button with precise force |
+
+---
+
+## Data Format
+
+Each task is provided in two formats:
+
+- **`<task>.zarr/`** — Zarr v2 directory store, ready for direct training use
+- **`<task>.zip`** — Zipped archive of the same zarr store
+- **`<task>_normalizer.json`** — Pre-computed normalizer statistics (mean/std) for all fields
+
+### Zarr Structure
+
+```
+<task>.zarr/
+├── data/
+│   ├── action          (N, 6)   float32  — end-effector delta pose (6-DOF)
+│   ├── pos             (N, 6)   float32  — end-effector absolute pose
+│   ├── force           (N, 6)   float32  — raw F/T sensor readings
+│   ├── delta_force     (N, 6)   float32  — force delta (not in `peel`)
+│   ├── gripper_action  (N, 1)   float32  — gripper command (0=open, 1=close)
+│   ├── gripper_state   (N, 1)   float32  — gripper current state
+│   ├── rgb_arm         (N, 3, 240, 320)  uint8 — wrist camera (JPEG-compressed)
+│   └── rgb_fix         (N, 3, 240, 320)  uint8 — fixed camera (JPEG-compressed)
+└── meta/
+    └── episode_ends    (E,)     uint32   — cumulative step index at each episode end
+```
+
+> **Note:** The `peel` task does not contain the `delta_force` field.
+
+RGB arrays are stored with a custom JPEG codec. To read them, install [image_codecs](https://github.com/JokerESC/ForceFlow/tree/main/CleanDiffuser/image_codecs) from the ForceFlow repo and register the codec before opening the zarr store.
+
+---
+
+## Usage
+
+### Prerequisites
+
+```bash
+git clone --recurse-submodules https://github.com/JokerESC/ForceFlow.git
+cd ForceFlow
+pip install -r requirements.txt
+pip install -e CleanDiffuser/
+```
+
+### Load a dataset
+
+```python
+import sys
+sys.path.insert(0, 'path/to/ForceFlow/CleanDiffuser')
+
+import numcodecs
+import image_codecs
+numcodecs.register_codec(image_codecs.jpeg)
+
+import zarr
+import numpy as np
+
+z = zarr.open('plug.zarr', 'r')
+
+episode_ends = z['meta/episode_ends'][:]   # shape (100,)
+actions      = z['data/action'][:]         # shape (50107, 6)
+forces       = z['data/force'][:]          # shape (50107, 6)
+rgb_arm      = z['data/rgb_arm'][:]        # shape (50107, 3, 240, 320)
+
+# Reconstruct per-episode slices
+starts = np.concatenate([[0], episode_ends[:-1]])
+for ep_idx, (s, e) in enumerate(zip(starts, episode_ends)):
+    ep_actions = actions[s:e]   # (T, 6)
+    ep_forces  = forces[s:e]    # (T, 6)
+```
+
+### Training with ForceFlow
+
+```bash
+# Edit configs/xarm.yaml to point to the downloaded data
+python -m pipeline.train --config configs/xarm.yaml
+```
+
+---
+
+## Hardware
+
+| Component | Details |
+|---|---|
+| Robot arm | UFACTORY xArm6 |
+| F/T sensor | 6-axis wrist force/torque sensor |
+| Wrist camera | Intel RealSense D435 |
+| Fixed camera | Intel RealSense L515 |
+| Teleoperation | 3Dconnexion SpaceMouse |
+
+---
+
+## License
+
+MIT — see [LICENSE](https://github.com/JokerESC/ForceFlow/blob/main/LICENSE).
+
+---
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@misc{forceflow2025,
+  title  = {ForceFlow: Learning to Feel and Act via Contact-Driven Flow Matching},
+  author = {JokerESC},
+  year   = {2025},
+  url    = {https://github.com/JokerESC/ForceFlow}
+}
+```