---
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

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)}")