ariefansclub/humanoid-motion-embedding-transformer-v1

Humanoid Motion Embedding Transformer

Overview

Humanoid Motion Embedding Transformer (HMET) is a lightweight representation model designed to encode humanoid motion sequences into structured latent embeddings.

The model focuses on transforming joint-angle trajectories and sensor streams into compact motion representations that can be reused for planning, imitation learning, and behavior similarity analysis.

Architecture

• Transformer encoder backbone
• Positional encoding for temporal motion steps
• Motion-token embedding layer
• 128-d latent motion representation

Intended Use

• Motion similarity search
• Behavior clustering
• Pre-training for planning systems
• Robotics representation research

Input Format

Time-series motion frames:

• joint_positions
• joint_velocities
• imu_data

Output

Fixed-length motion embedding vector.

Research Contribution

Provides a standardized embedding layer for cross-robot motion representation alignment.

License

MIT