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---
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license: openrail
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tags:
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- robotics
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- trajectory-prediction
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- manipulation
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- computer-vision
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- time-series
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pretty_name: Codatta Robotic Manipulation Trajectory
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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dataset_info:
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features:
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- name: id
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dtype: string
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- name: total_frames
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dtype: int32
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- name: annotations
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dtype: string
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- name: trajectory_image
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dtype: image
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- name: video_path
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dtype: string
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splits:
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- name: train
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num_bytes: 39054025
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num_examples: 50
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download_size: 38738419
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dataset_size: 39054025
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language:
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- en
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size_categories:
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- n<1K
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---
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# Codatta Robotic Manipulation Trajectory (Sample) |
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## Dataset Summary |
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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. |
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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. |
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**Note:** This is a sample dataset containing **50 annotated examples**. |
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## Supported Tasks |
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* **Trajectory Prediction:** Predicting the path of a gripper based on visual context. |
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* **Keyframe Extraction:** Identifying critical moments in a manipulation task (e.g., contact, velocity change). |
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* **Robotic Control:** Imitation learning from human-demonstrated or teleoperated data. |
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## Dataset Structure |
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### Data Fields |
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* **`id`** (string): Unique identifier for the trajectory sequence. |
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* **`total_frames`** (int32): Total number of frames in the video sequence. |
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* **`video_path`** (string): Path to the source MP4 video file recording the manipulation action. |
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* **`trajectory_image`** (image): A JPEG preview showing the overlaid trajectory path or keyframe visualization. |
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* **`annotations`** (string): A JSON-formatted string containing the detailed coordinate data. |
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* *Structure:* Contains lists of keyframes, timestamp, and the 5-point coordinates for the gripper in each annotated frame. |
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### Data Preview |
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*(Hugging Face's viewer will automatically render the `trajectory_image` here)* |
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## Annotation Standards |
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The data was annotated following a strict protocol to ensure precision and consistency. |
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### 1. Viewpoint Scope |
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* **Included:** Third-person views (fixed camera recording the robot). |
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* [cite_start]**Excluded:** First-person views (Eye-in-Hand) are explicitly excluded to ensure consistent coordinate mapping[cite: 5, 15]. |
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### 2. Keyframe Selection |
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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]: |
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1. [cite_start]**Start Frame:** The gripper first appears in the screen[cite: 21]. |
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2. [cite_start]**End Frame:** The gripper leaves the screen[cite: 22]. |
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3. [cite_start]**Velocity Change:** Frames where the speed direction suddenly changes (marking the minimum speed point)[cite: 23]. |
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4. [cite_start]**State Change:** Frames where the gripper opens or closes[cite: 24]. |
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5. [cite_start]**Contact:** The precise moment the gripper touches the object[cite: 25]. |
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### 3. The 5-Point Annotation Method |
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[cite_start]For every annotated keyframe, the gripper is labeled with **5 specific coordinate points** to capture its pose and state accurately[cite: 27]: |
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| Point ID | Description | Location Detail | |
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| :--- | :--- | :--- | |
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| **Point 1 & 2** | **Fingertips** | [cite_start]Center of the bottom edge of the gripper tips[cite: 28, 29]. | |
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| **Point 3 & 4** | **Gripper Ends** | [cite_start]The rearmost points of the closing area (indicating the finger direction)[cite: 31]. | |
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| **Point 5** | **Tiger's Mouth** | [cite_start]The center of the crossbeam (base of the gripper)[cite: 32]. | |
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### 4. Quality Control |
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* [cite_start]**Accuracy:** All datasets passed a rigorous quality assurance process with a minimum **95% accuracy rate**[cite: 78]. |
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* **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]. |
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## Usage Example |
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```python |
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from datasets import load_dataset |
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import json |
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# Load the dataset |
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ds = load_dataset("Codatta/robotic-manipulation-trajectory", split="train") |
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# Access a sample |
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sample = ds[0] |
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# View the image |
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print(f"Trajectory ID: {sample['id']}") |
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sample['trajectory_image'].show() |
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# Parse annotations |
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annotations = json.loads(sample['annotations']) |
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print(f"Keyframes count: {len(annotations)}") |
