README.md

---
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.

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## 📊 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)

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## 🛠 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.

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## 🔬 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 |

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## 🚀 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.