AnodeAI/Humanoid-Kinetic-Fleet

🤖 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

🚀 Usage

This dataset is optimized for:

1. 1.Reinforcement Learning (RL): Training humanoids for object manipulation using mass/torque metadata.
2. 2.Edge-Case Detection: Testing model failure points in low-light/high-clutter scenarios.
3. 3.Sensor Fusion: Aligning 24GHz Radar returns with LiDAR point clouds.