Privacy-Preserving Knowledge Fusion in HY-Embodied-0.5 via Secure Domain Integration

TL;DR: How can robots learn from both private (e.g., company or personal) and public data without leaking sensitive information? Inspired by the “Wenlu” architecture (Geng, 2025), we’ll build a privacy-aware HY-Embodied-0.5 that safely fuses proprietary knowledge and public models for closed-loop decision-making.

Research Question: Can a secure, multimodal knowledge fusion framework enable HY-Embodied-0.5 to leverage private domain-specific information alongside public foundation models without compromising privacy or performance?

Hypothesis: A brain-inspired tagging and replay mechanism, combined with secure knowledge fusion, will allow HY-Embodied-0.5 to efficiently and safely integrate private data, enhancing specialized performance while maintaining robust generalization.

Experiment Plan: - Design and implement a privacy-preserving memory module (e.g., with federated learning or differential privacy) inspired by the Wenlu system.

• Integrate this with the existing HY-Embodied-0.5 pipeline.
• Test on industry-relevant tasks (e.g., warehouse automation, healthcare robotics) requiring both general and confidential knowledge.
• Evaluate on performance, privacy leakage risk, and adaptability to new private data.
• Compare to baseline models without secure integration.

References:

• Geng, L. (2025). A "Wenlu" Brain System for Multimodal Cognition and Embodied Decision-Making: A Secure New Architecture for Deep Integration of Foundation Models and Domain Knowledge. arXiv.org.
• X. TencentRobotics et al. (2026). HY-Embodied-0.5: Embodied Foundation Models for Real-World Agents.