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README.md

@@ -1,153 +1,126 @@
 ---
 license: apache-2.0
 task_categories:
-- robotics
+  - robotics
+  - reinforcement-learning
 tags:
-- LeRobot
-- robotics
-- imitation-learning
-- diffusion-policy
-- manipulation
-- fetch
-configs:
-- config_name: default
-  data_files: data/*/*.parquet
+  - robotics
+  - imitation-learning
+  - diffusion-policy
+  - manipulation
+  - fetch
+  - mujoco
+  - lerobot
+size_categories:
+  - 1K<n<10K
 ---
 
-This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
-
-
-<a class="flex" href="https://huggingface.co/spaces/lerobot/visualize_dataset?path=e-cagan/diffpick">
-<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl.svg"/>
-<img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl-dark.svg"/>
-</a>
-
-
-## Dataset Description
-
-
-
-- **Homepage:** [More Information Needed]
-- **Paper:** [More Information Needed]
-- **License:** apache-2.0
-
-## Dataset Structure
-
-[meta/info.json](meta/info.json):
-```json
-{
-    "codebase_version": "v3.0",
-    "fps": 25,
-    "features": {
-        "observation.image": {
-            "dtype": "video",
-            "shape": [
-                96,
-                96,
-                3
-            ],
-            "names": [
-                "height",
-                "width",
-                "channels"
-            ],
-            "info": {
-                "video.height": 96,
-                "video.width": 96,
-                "video.codec": "av1",
-                "video.pix_fmt": "yuv420p",
-                "video.is_depth_map": false,
-                "video.fps": 25,
-                "video.channels": 3,
-                "has_audio": false
-            }
-        },
-        "observation.state": {
-            "dtype": "float32",
-            "shape": [
-                10
-            ],
-            "names": [
-                "gripper_x",
-                "gripper_y",
-                "gripper_z",
-                "finger_left",
-                "finger_right",
-                "gripper_vx",
-                "gripper_vy",
-                "gripper_vz",
-                "finger_left_v",
-                "finger_right_v"
-            ]
-        },
-        "action": {
-            "dtype": "float32",
-            "shape": [
-                4
-            ],
-            "names": [
-                "dx",
-                "dy",
-                "dz",
-                "gripper_cmd"
-            ]
-        },
-        "timestamp": {
-            "dtype": "float32",
-            "shape": [
-                1
-            ],
-            "names": null
-        },
-        "frame_index": {
-            "dtype": "int64",
-            "shape": [
-                1
-            ],
-            "names": null
-        },
-        "episode_index": {
-            "dtype": "int64",
-            "shape": [
-                1
-            ],
-            "names": null
-        },
-        "index": {
-            "dtype": "int64",
-            "shape": [
-                1
-            ],
-            "names": null
-        },
-        "task_index": {
-            "dtype": "int64",
-            "shape": [
-                1
-            ],
-            "names": null
-        }
-    },
-    "total_episodes": 200,
-    "total_frames": 5489,
-    "total_tasks": 1,
-    "chunks_size": 1000,
-    "data_files_size_in_mb": 100,
-    "video_files_size_in_mb": 200,
-    "data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
-    "video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4",
-    "robot_type": "fetch",
-    "splits": {
-        "train": "0:200"
-    }
-}
+# DiffPick: Fetch Pick-and-Place Demonstrations
+
+A clean dataset of **200 successful pick-and-place demonstrations** collected from a scripted expert policy in the [`FetchPickAndPlace-v4`](https://robotics.farama.org/envs/fetch/pick_and_place/) MuJoCo environment. Designed for training **vision-based imitation learning** policies (Diffusion Policy, ACT, BC).
+
+Part of the [DiffPick project](https://github.com/e-cagan/diffpick) — a from-scratch implementation of a Diffusion Policy pipeline with ROS2 deployment.
+
+## Dataset Stats
+
+| Property | Value |
+|---|---|
+| Episodes | 200 |
+| Total frames | 5,489 |
+| Mean episode length | 27.4 steps |
+| Min / Max length | 20 / 35 steps |
+| FPS | 25 |
+| Image resolution | 96×96 RGB |
+| Success rate (during collection) | 97.1% (200 of 206 attempts kept) |
+
+## Features
+
+| Key | Shape | Type | Description |
+|---|---|---|---|
+| `observation.image` | (3, 96, 96) | float32 | Front-view RGB camera |
+| `observation.state` | (10,) | float32 | Robot proprioception only (gripper xyz, finger widths, velocities). **No object pose** — must be inferred from image. |
+| `action` | (4,) | float32 | End-effector delta (dx, dy, dz) ∈ [-1,1] + gripper command (-1 close, +1 open) |
+| `task` | string | — | "Pick up the block and place it at the target location." |
+
+### Why proprioception-only state?
+
+The state vector deliberately **excludes object position**. This forces a learned policy to develop visual grounding rather than copying ground-truth coordinates. The result: policies trained on this dataset must actually *see* the object in the RGB stream to succeed — closer to a real-world deployment scenario where object pose isn't directly observable.
+
+## Expert Policy
+
+Demonstrations were generated by a hand-crafted state machine controller:
+
+```
+APPROACH (gripper open, hover above object)
+↓
+DESCEND (gripper open, lower to object)
+↓
+GRASP (close gripper, hold for 8 steps)
+↓
+PLACE (move to target, gripper closed)
+```
+
+Proportional control in end-effector space (no IK required, since the env exposes a 4-D end-effector action interface). Episodes ending in success too quickly (< 15 steps, indicating object near target at reset) were filtered out.
+
+Source: [`data_collection/scripted_policy.py`](https://github.com/e-cagan/diffpick/blob/main/data_collection/scripted_policy.py)
+
+## Usage
+
+```python
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+dataset = LeRobotDataset("e-cagan/diffpick")
+sample = dataset[0]
+
+print(sample["observation.image"].shape)  # torch.Size([3, 96, 96])
+print(sample["observation.state"].shape)  # torch.Size([10])
+print(sample["action"].shape)             # torch.Size([4])
+```
+
+## Reproduction
+
+```bash
+git clone https://github.com/e-cagan/diffpick
+cd diffpick
+pip install -r requirements.txt
+
+# Collect raw demos
+python -m data_collection.collect --n_episodes 200
+
+# Convert to LeRobotDataset format
+python -m data_collection.to_lerobot_dataset \
+    --raw_dir data/raw_demos \
+    --repo_id <your-username>/diffpick \
+    --fps 25
 ```
 
+## Intended Use
+
+- Training **Diffusion Policy** for vision-conditioned manipulation
+- Benchmarking imitation learning algorithms (BC vs ACT vs DP)
+- Learning resource for ROS2 + MuJoCo + LeRobot integration
+
+## Limitations
+
+- Single environment seed family (`FetchPickAndPlace-v4` defaults). No domain randomization for backgrounds, lighting, or distractors.
+- Single front-facing 96×96 camera. No wrist cam, no depth.
+- Scripted expert is deterministic given a seed — no behavioral diversity (no left-hand/right-hand approach modes, etc.). This may limit the multi-modal advantages of Diffusion Policy.
+- Object is a single blue cube. No category generalization.
 
 ## Citation
 
-**BibTeX:**
+If you use this dataset, please cite:
 
 ```bibtex
-[More Information Needed]
-```
+@misc{apaydin2026diffpick,
+  author = {Apaydın, Emin Çağan},
+  title = {DiffPick: A Diffusion Policy Pipeline for Fetch Pick-and-Place},
+  year = {2026},
+  publisher = {GitHub},
+  url = {https://github.com/e-cagan/diffpick}
+}
+```
+
+## License
+
+Apache 2.0