achiepatricia/han-cross-domain-generalization-core-v1

Humanoid Cross-Domain Generalization Core

This model enables humanoid agents to generalize knowledge and skills across different domains, tasks, and environments within decentralized systems.

It reduces retraining requirements and allows rapid adaptation to unseen operational contexts.

Objective

To provide strong cross-domain transfer ability for humanoid agents operating in heterogeneous real-world scenarios.

Architecture

• —Unified Multimodal Encoder
• —Domain Abstraction Layer
• —Transfer Learning Adapter Blocks
• —Context Reweighting Module
• —Generalization Stability Head

Capabilities

• —Zero-shot task adaptation
• —Cross-domain skill transfer
• —Context-aware representation shifting
• —Reduced fine-tuning dependency
• —Foundation-level reasoning core

Training Strategy

• —Multi-domain pretraining
• —Contrastive representation alignment
• —Meta-learning adaptation loops
• —Distributed fine-tuning compatibility

Designed For

Large-scale decentralized humanoid deployment requiring domain flexibility and long-term scalability.

Part of

Humanoid Network (HAN)

License

MIT