| --- |
| 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) |
|
|
| ## Overview |
|
|
| 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. |
|
|
| 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. |
|
|
| ## Dataset Contents |
|
|
| 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. It contains lists of keyframes, timestamps, and 5-point coordinates for the gripper. |
| |
| ### Annotation Standards |
| The data follows a strict protocol to ensure precision: |
| |
| **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. |
| |
| **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: |
| |
| | 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). | |
| |
| **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. |
| |
| ## Key Statistics |
| |
| * **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: |
| |
| * **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 |
| |
| ```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)}") |
| ``` |
| |
| ## License and Open-Source Details |
| |
| * **License:** This dataset is released under the **OpenRAIL** license. |
|
|
