Update README.md

README.md

@@ -1,5 +1,12 @@
 ---
 license: openrail
+tags:
+- robotics
+- trajectory-prediction
+- manipulation
+- computer-vision
+- time-series
+pretty_name: Codatta Robotic Manipulation Trajectory
 configs:
 - config_name: default
   data_files:
@@ -23,4 +30,87 @@ dataset_info:
     num_examples: 50
   download_size: 38738419
   dataset_size: 39054025
+language:
+- en
+size_categories:
+- n<1K
 ---
+
+# Codatta Robotic Manipulation Trajectory (Sample)
+
+## Dataset Summary
+
+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.
+
+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.
+
+**Note:** This is a sample dataset containing **50 annotated examples**.
+
+## 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
+
+### 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.
+
+### Data Preview
+*(Hugging Face's viewer will automatically render the `trajectory_image` here)*
+
+## Annotation Standards
+
+The data was annotated following a strict protocol to ensure precision and consistency.
+
+### 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].
+
+### 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. 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]:
+
+| 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]. |
+
+### 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].
+
+## Usage Example
+
+```python
+from datasets import load_dataset
+import json
+
+# Load the dataset
+ds = load_dataset("Codatta/robotic-manipulation-trajectory", split="train")
+
+# Access a sample
+sample = ds[0]
+
+# View the image
+print(f"Trajectory ID: {sample['id']}")
+sample['trajectory_image'].show()
+
+# Parse annotations
+annotations = json.loads(sample['annotations'])
+print(f"Keyframes count: {len(annotations)}")