Update README.md

README.md

@@ -1,101 +1,102 @@
----
-license: openrail
-tags:
-- robotics
-- trajectory-prediction
-- manipulation
-- computer-vision
-- time-series
-pretty_name: Codatta Robotic Manipulation Trajectory
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: total_frames
-    dtype: int32
-  - name: annotations
-    dtype: string
-  - name: trajectory_image
-    dtype: image
-  - name: video_path
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 39054025
-    num_examples: 50
-  download_size: 38738419
-  dataset_size: 39054025
-language:
-- en
-size_categories:
-- n<1K
----
+---
+license: openrail
+tags:
+- robotics
+- trajectory-prediction
+- manipulation
+- computer-vision
+- time-series
+pretty_name: Codatta Robotic Manipulation Trajectory
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: total_frames
+    dtype: int32
+  - name: annotations
+    dtype: string
+  - name: trajectory_image
+    dtype: image
+  - name: video_path
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 39054025
+    num_examples: 50
+  download_size: 38738419
+  dataset_size: 39054025
+language:
+- en
+size_categories:
+- n<1K
+---
 
 # Codatta Robotic Manipulation Trajectory (Sample)
 
-## Dataset Summary
+## Overview
 
-This dataset contains high-quality annotated trajectories of robotic gripper manipulations. It is designed to train models for fine-grained control, trajectory prediction, and object interaction tasks.
+This dataset contains high-quality annotated trajectories of robotic gripper manipulations. Produced by **Codatta**, it focuses on third-person views of robotic arms performing pick-and-place or manipulation tasks. The dataset is designed to train models for fine-grained control, trajectory prediction, and object interaction tasks.
 
-Produced by **Codatta**, this dataset focuses on third-person views of robotic arms performing pick-and-place or manipulation tasks. Each sample includes the raw video, a visualization of the trajectory, and a rigorous JSON annotation of keyframes and coordinate points.
+The scope specifically includes third-person views (fixed camera recording the robot) while explicitly excluding first-person views (Eye-in-Hand) to ensure consistent coordinate mapping.
 
-**Note:** This is a sample dataset containing **50 annotated examples**.
+## Dataset Contents
 
-## Supported Tasks
-
-* **Trajectory Prediction:** Predicting the path of a gripper based on visual context.
-* **Keyframe Extraction:** Identifying critical moments in a manipulation task (e.g., contact, velocity change).
-* **Robotic Control:** Imitation learning from human-demonstrated or teleoperated data.
-
-## Dataset Structure
+Each sample in this dataset includes the raw video, a visualization of the trajectory, and a rigorous JSON annotation of keyframes and coordinate points.
 
 ### Data Fields
-
 * **`id`** (string): Unique identifier for the trajectory sequence.
 * **`total_frames`** (int32): Total number of frames in the video sequence.
 * **`video_path`** (string): Path to the source MP4 video file recording the manipulation action.
 * **`trajectory_image`** (image): A JPEG preview showing the overlaid trajectory path or keyframe visualization.
-* **`annotations`** (string): A JSON-formatted string containing the detailed coordinate data. 
-    * *Structure:* Contains lists of keyframes, timestamp, and the 5-point coordinates for the gripper in each annotated frame.
+* **`annotations`** (string): A JSON-formatted string containing the detailed coordinate data. It contains lists of keyframes, timestamps, and 5-point coordinates for the gripper.
 
-### Data Preview
-*(Hugging Face's viewer will automatically render the `trajectory_image` here)*
+### Annotation Standards
+The data follows a strict protocol to ensure precision:
 
-## Annotation Standards
+**1. Keyframe Selection**
+Annotations are sparse, focusing on specific Keyframes defined by the following events:
+* **Start Frame:** The gripper first appears in the screen.
+* **End Frame:** The gripper leaves the screen.
+* **Velocity Change:** Frames where the speed direction suddenly changes (marking the minimum speed point).
+* **State Change:** Frames where the gripper opens or closes.
+* **Contact:** The precise moment the gripper touches the object.
 
-The data was annotated following a strict protocol to ensure precision and consistency.
+**2. The 5-Point Annotation Method**
+For every annotated keyframe, the gripper is labeled with **5 specific coordinate points** to capture its pose and state accurately:
 
-### 1. Viewpoint Scope
-* **Included:** Third-person views (fixed camera recording the robot).
-* [cite_start]**Excluded:** First-person views (Eye-in-Hand) are explicitly excluded to ensure consistent coordinate mapping[cite: 5, 15].
+| Point ID | Description | Location Detail |
+| :--- | :--- | :--- |
+| **Point 1 & 2** | **Fingertips** | Center of the bottom edge of the gripper tips. |
+| **Point 3 & 4** | **Gripper Ends** | The rearmost points of the closing area (indicating the finger direction). |
+| **Point 5** | **Tiger's Mouth** | The center of the crossbeam (base of the gripper). |
 
-### 2. Keyframe Selection
-Annotations are not dense (every frame) but sparse, focusing on **Keyframes** that define the motion logic. [cite_start]A Keyframe is defined by the following events [cite: 20-25]:
-1.  [cite_start]**Start Frame:** The gripper first appears in the screen[cite: 21].
-2.  [cite_start]**End Frame:** The gripper leaves the screen[cite: 22].
-3.  [cite_start]**Velocity Change:** Frames where the speed direction suddenly changes (marking the minimum speed point)[cite: 23].
-4.  [cite_start]**State Change:** Frames where the gripper opens or closes[cite: 24].
-5.  [cite_start]**Contact:** The precise moment the gripper touches the object[cite: 25].
+**3. Quality Control**
+* **Accuracy:** All datasets passed a rigorous quality assurance process with a minimum **95% accuracy rate**.
+* **Occlusion Handling:** Sequences where the gripper is fully occluded or only shows a side profile without clear features are discarded.
 
-### 3. The 5-Point Annotation Method
-[cite_start]For every annotated keyframe, the gripper is labeled with **5 specific coordinate points** to capture its pose and state accurately[cite: 27]:
+## Key Statistics
 
-| Point ID | Description | Location Detail |
-| :--- | :--- | :--- |
-| **Point 1 & 2** | **Fingertips** | [cite_start]Center of the bottom edge of the gripper tips[cite: 28, 29]. |
-| **Point 3 & 4** | **Gripper Ends** | [cite_start]The rearmost points of the closing area (indicating the finger direction)[cite: 31]. |
-| **Point 5** | **Tiger's Mouth** | [cite_start]The center of the crossbeam (base of the gripper)[cite: 32]. |
+* **Total Examples:** 50 annotated examples (Sample Dataset).
+* **Language:** English (`en`).
+* **Splits:** Train split available.
+* **Download Size:** ~38.7 MB.
+* **Dataset Size:** ~39.0 MB.
+
+## Usage
+
+This dataset is suitable for research and development in the field of Embodied AI and Computer Vision. It is specifically curated to support the following downstream tasks and application scenarios:
 
-### 4. Quality Control
-* [cite_start]**Accuracy:** All datasets passed a rigorous quality assurance process with a minimum **95% accuracy rate**[cite: 78].
-* **Occlusion Handling:** If the gripper is partially occluded, points are estimated based on object geometry. [cite_start]Sequences where the gripper is fully occluded or only shows a side profile without clear features are discarded[cite: 58, 63].
+* **Trajectory Prediction:** The high-precision coordinate data allows for training models to predict the future path of a gripper based on initial visual contexts.
+* **Keyframe Extraction & Event Detection:** By leveraging the labeled event types (e.g., "Contact", "Velocity Change"), models can be trained to automatically identify critical moments in long-horizon manipulation tasks.
+* **Fine-Grained Robotic Control:** The 5-point annotation system provides detailed pose information, enabling Imitation Learning (IL) from human-demonstrated or teleoperated data for precise pick-and-place operations.
+* **Object Interaction Analysis:** The dataset helps in understanding gripper-object relationships, specifically modeling the transition states when the gripper opens, closes, or makes contact with an object.
 
-## Usage Example
+### Usage Example
 
 ```python
 from datasets import load_dataset
@@ -113,4 +114,9 @@ sample['trajectory_image'].show()
 
 # Parse annotations
 annotations = json.loads(sample['annotations'])
-print(f"Keyframes count: {len(annotations)}")
+print(f"Keyframes count: {len(annotations)}")
+```
+
+## License and Open-Source Details
+
+* **License:** This dataset is released under the **OpenRAIL** license.