Dataset release

.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+splits/1frame/train.json filter=lfs diff=lfs merge=lfs -text
+splits/3frame-thinking/test.json filter=lfs diff=lfs merge=lfs -text
+splits/3frame-thinking/train.json filter=lfs diff=lfs merge=lfs -text
+splits/3frame-thinking/val.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+task_categories:
+- robotics
+- image-to-text
+- reinforcement-learning
+tags:
+- robot-manipulation
+- reward-model
+- vision-language
+- vlm
+- dense-reward
+pretty_name: DenseReward
+size_categories:
+- 500K<n<1M
+---
+
+# DenseReward Dataset
+
+🌐 [Project page](https://dense-reward.github.io/) · 📄 [Paper (arXiv:2607.13033)](https://arxiv.org/abs/2607.13033)
+
+This is the dataset for **DenseReward: Dense Reward Learning via Failure Synthesis for Robotic Manipulation**. It pairs single robot-manipulation frames (and short chronological frame windows) with a scalar task-progress reward in `[0.000, 1.000]`, used to finetune a vision-language reward model.
+
+**Models trained on this data:**
+- [`densereward/densereward-1frame`](https://huggingface.co/densereward/densereward-1frame): single-frame reward model — one RGB frame + task text → scalar reward.
+- [`densereward/densereward-3frame-thinking`](https://huggingface.co/densereward/densereward-3frame-thinking): 3-frame reward model with reasoning — 3 chronological frames + task text → a `<think>` reasoning word, then a scalar reward.
+
+## Dataset structure
+
+```
+data/
+  droid.zip
+  isaac.zip
+  robosuite.zip
+  libero.zip
+splits/
+  1frame/{train,val,test}.json          # 1 image per sample -> reward
+  3frame-thinking/{train,val,test}.json # 3 chronological images per sample -> <think>reason</think> + reward
+  manifest.json                          # trajectory-level train/val/test assignment
+```
+
+Each source's images ship as a zip in `data/`. Unzip each one in place so it expands to `data/droid/*.jpg`, matching the paths referenced in `splits/*/*.json`.
+
+
+## Data format
+
+Each split file is a JSON list of samples in a simple SFT conversation format:
+
+**`splits/1frame/*.json`**: one image, plain reward target
+```json
+{
+  "conversation": [
+    {"from": "human", "value": "<image>put the alcohol on the plate"},
+    {"from": "assistant", "value": "0.327"}
+  ],
+  "images": ["data/isaac/isaac_alcohol_0_collision_000_frame_000300.jpg"]
+}
+```
+
+**`splits/3frame-thinking/*.json`** : 3 chronological images (oldest → current; early frames in a trajectory repeat the first frame to pad the window), a reasoning word, then the reward for the last frame. Here the reward drops (0.341 → 0.327) as the robot collides with the object, and the reasoning word reflects it:
+```json
+{
+  "conversation": [
+    {"from": "human", "value": "<image><image><image>put the alcohol on the plate"},
+    {"from": "assistant", "value": "<think>\ncollision\n</think>\n\n0.327"}
+  ],
+  "images": [
+    "data/isaac/isaac_alcohol_0_collision_000_frame_000240.jpg",
+    "data/isaac/isaac_alcohol_0_collision_000_frame_000270.jpg",
+    "data/isaac/isaac_alcohol_0_collision_000_frame_000300.jpg"
+  ]
+}
+```
+
+The `<think>` vocabulary is `correct | miss | collision | fall | not smooth |
+failure`.
+
+
+## License
+
+Released under Apache License 2.0 for this repository.
+
+## Citation
+
+```bibtex
+@article{fang2026densereward,
+    title={DenseReward: Dense Reward Learning via Failure Synthesis for Robotic Manipulation},
+    author={Fang, Yu and Dong, Wanxi and Liu, Jiaqi and Yang, Yue and Huo, Mingxiao and Mu, Yao and Yao, Huaxiu and Li, Li Erran and Szafir, Daniel and Ding, Mingyu},
+    journal={arXiv preprint arXiv:2607.13033},
+    year={2026}
+}
+```

data/droid.zip

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