Embodied AGI for Robotics

Our core objective is to pioneer a pathway towards "natural-like" Artificial General Intelligence (AGI) for robotics, moving beyond statistical pattern matching to achieve genuine understanding, causality, and adaptability.

This involves developing systems that can interpret high-level, multimodal human intent and translate it into precise, intelligent actions through a synergistic integration of:

• Dr. Howard Schneider's Causal Cognitive Architecture (CCA)
• Google's Gemini API for semantic cognition
• Professional-grade digital twin development toolchain

We envision a future where robots are not just automated machines but truly intelligent agents capable of:

Our comprehensive plan is structured around four interconnected research pillars:

While our vision is ambitious, we recognize critical challenges that require focused, long-term research:

• CCA Empirical Validation: Extensive validation on large-scale, standardized benchmarks to prove robustness and performance
• Scalability of Navigation Maps: Addressing computational and memory challenges for practical edge device implementation
• Primitive Acquisition: Developing robust mechanisms for autonomous learning and discovery of primitives
• Semantically-Aware Compression: Revolutionary approach to data compression preserving task-critical information
• Verifiable Handoff Protocols: Ensuring safe transitions between edge CCA and cloud-based semantic models