Add dataset card: description, fields, splits, licence, citation

README.md

@@ -1,4 +1,19 @@
 ---
+license: cc-by-4.0
+pretty_name: SGE-Goal BridgeDataV2 (teleoperated)
+task_categories:
+- image-to-image
+- robotics
+language:
+- en
+size_categories:
+- 10K<n<100K
+tags:
+- robotics
+- manipulation
+- goal-image-generation
+- image-editing
+- bridgedata-v2
 dataset_info:
   features:
   - name: id
@@ -35,3 +50,99 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+# SGE-Goal BridgeDataV2 (teleoperated)
+
+Training and evaluation data for **SGE-Goal**, from *Goal Images Are Control Targets:
+Spatially-Grounded Goal-Image Synthesis for Robotic Manipulation* (WACV 2027).
+
+- Code: https://github.com/Riddhi-Chatterjee/SGE-Goal
+- Project page: https://riddhi-chatterjee.github.io/SGE-Goal/
+- Model checkpoints: https://huggingface.co/RiddhiCh/SGE-Goal
+
+Each sample turns one atomic manipulation step from a robot episode into an image-editing example:
+the scene before the step, the scene after it, where the edit happens, and the instruction. The
+samples were extracted automatically from [BridgeData V2](https://rail-berkeley.github.io/bridgedata/), teleoperated trajectories, without manual annotation.
+
+## Fields
+
+| Field | Type | Description |
+|---|---|---|
+| `id` | string | Sample identifier: the source trajectory path, e.g. `bridge_data_v2_datacol2_toykitchen2_many_skills_34_2023-03-09_12-34-29_raw_traj_group0_traj9_images0` |
+| `original_image` | image | Observation before the manipulation step (`I_o`) |
+| `edited_image` | image | Observation after the step, the goal image (`I_e`) |
+| `initial_edit_region_mask` | image | Binary mask of the manipulated objects before the step (`M_i`) |
+| `final_edit_region_mask` | image | Binary mask of where those objects are after the step (`M_f`) |
+| `edit_instruction` | string | The atomic manipulation instruction (`T`) |
+
+Images are 640 × 480 pixels; masks are white (edit region) on black.
+
+About half of the instructions keep the annotation-confidence line from BridgeData V2's language files (for example `open the silver pot …\nconfidence: 0.2`). The released SGE-Goal models were trained on the field as-is; strip the line if you need plain instructions.
+
+## Splits
+
+| Split | Samples |
+|---|---|
+| train | 40,851 |
+| validation | 1,000 |
+| test | 2,000 |
+
+Samples are assigned to splits by MD5 hashing of their identifier (seed 42).
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("RiddhiCh/LGRM_BridgeDataV2_Teleoperated")
+sample = ds["train"][0]
+sample["original_image"]      # PIL.Image
+sample["edit_instruction"]    # str
+```
+
+## How the data was extracted
+
+The SGE-Goal data pipeline chains open vision models on each episode:
+
+1. robot-arm masking and reference-frame selection (EVF-SAM, verified with BLIP-2),
+2. class-agnostic object proposals on the reference frame (SAM),
+3. object tracking through the episode (XMem),
+4. separation of moved and stationary objects with LoFTR matching and 2-means clustering,
+   followed by edit-mask generation and selection of the edited frame.
+
+For episodes without a language instruction, VideoChat-Flash writes one from the video. The
+pipeline is in [`data_pipeline/`](https://github.com/Riddhi-Chatterjee/SGE-Goal/tree/main/data_pipeline).
+In a study with 50 annotators rating 500 tuples per dataset (1–5 scale), the lowest mean rating of
+any extracted component across the three datasets was 4.44.
+
+## Intended use
+
+Training and evaluating goal-image generators and language-guided image editing for robotic
+manipulation. Masks and edited frames come from an automatic pipeline: they are reliable in
+aggregate, but individual samples can be imperfect, typically with heavily occluded targets or
+several near-identical objects.
+
+## Licence
+
+BridgeData V2 is released under the Creative Commons Attribution 4.0 International License; this derived dataset is released under the same licence.
+
+## Citation
+
+If you use this dataset, please cite SGE-Goal and the source dataset. The SGE-Goal entry is
+temporary until the proceedings are published.
+
+```bibtex
+@inproceedings{chatterjee2027goalimages,
+  title     = {Goal Images Are Control Targets: Spatially-Grounded Goal-Image Synthesis for Robotic Manipulation},
+  author    = {Chatterjee, Riddhi and Shrirao, Ritish and Mehrotra, Kushagra and Gopalakrishnan, Viswanath},
+  booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
+  year      = {2027}
+}
+
+@inproceedings{walke2023bridgedata,
+  title     = {BridgeData V2: A Dataset for Robot Learning at Scale},
+  author    = {Walke, Homer and Black, Kevin and Lee, Abraham and Kim, Moo Jin and Du, Max and Zheng, Chongyi and Zhao, Tony and Hansen-Estruch, Philippe and Vuong, Quan and He, Andre and Myers, Vivek and Fang, Kuan and Finn, Chelsea and Levine, Sergey},
+  booktitle = {Conference on Robot Learning (CoRL)},
+  year      = {2023}
+}
+```