| --- |
| task_categories: |
| - text-classification |
| license: apache-2.0 |
| tags: |
| - code |
| - Humanoid |
| - 6D pose |
| - point cloud |
| - Robotics |
| size_categories: |
| - 1k-10k |
| --- |
| |
| # 🤖 Anode AI: Humanoid Kinetic Fleet (v1.0) |
|
|
| **High-Fidelity Synthetic Tensors for Next-Gen Humanoid Perception & Control.** |
|
|
| Anode AI’s **Humanoid Kinetic Fleet** is a mathematically deterministic synthetic dataset designed to bridge the Sim2Real gap for domestic and industrial humanoid robotics. Unlike standard computer vision datasets, this collection includes full **6-DoF ground truth**, **kinematic torque vectors**, and **Gaussian stochastic noise** modeled on real-world 24GHz radar and LiDAR interference. |
|
|
| --- |
|
|
| ## 📊 Dataset Summary |
|
|
| - **Total Records:** 1,240,000+ Frames |
| - **Format:** `.jsonl.gz` (Compressed JSON Lines) |
| - **Capture Rate:** 90Hz (Temporal Coherence) |
| - **Domain:** Domestic Environments (Kitchen, Living Room, Dining) |
| - **Physics Engine:** Anode Mud Engine v2.1 (Euler Integration) |
|
|
| --- |
|
|
| ## 🛠 Data Structure & Schema |
|
|
| Each record contains a multi-modal snapshot of the robot's state and its environment. |
|
|
| ### 1. Robot Kinematics |
| - **6-DoF Pose:** Precise [x, y, z] and Quaternions for the base and end-effectors. |
| - **Joint Dynamics:** 18-axis joint angles and velocities. |
| - **Force Feedback:** Torque vectors (Nm) and gripper pressure (N). |
|
|
| ### 2. Semantic Intelligence |
| - **Object Metadata:** Includes `mass_kg` and `kinetic_energy_j` for interaction logic. |
| - **Intent Prediction:** Behavioral labels for dynamic entities (e.g., `Child_5yo_Running`). |
| - **Threat Vectors:** Closing speeds and potential impact time calculations. |
|
|
| ### 3. Sensor Fidelity (Stochastic Layer) |
| - **Gaussian Noise:** Modeled via Box-Muller transforms to simulate sensor jitter. |
| - **Domain Randomization:** Variable lighting (Lux), texture shifts, and color variations. |
|
|
| --- |
|
|
| ## 🔬 Technical Specifications |
|
|
| | Parameter | Specification | Logic | |
| | :--- | :--- | :--- | |
| | **Noise Model** | Gaussian (Box-Muller) | Sustainable Real-World Noise | |
| | **Physics Integration** | Euler (dt=0.1s) | Kinematic Continuity | |
| | **Integrity Check** | SHA-256 | Cryptographic Data Provenance | |
| | **Coordinate System** | RHS (Right-Handed) | Standard Robotics Convention | |
|
|
| --- |
|
|
| ## 🚀 Usage |
|
|
| This dataset is optimized for: |
| 1. **Reinforcement Learning (RL):** Training humanoids for object manipulation using mass/torque metadata. |
| 2. **Edge-Case Detection:** Testing model failure points in low-light/high-clutter scenarios. |
| 3. **Sensor Fusion:** Aligning 24GHz Radar returns with LiDAR point clouds. |
