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