Physical AI represents a fundamental shift from traditional digital-only artificial intelligence to systems that operate within the constraints and opportunities of the physical world.
Our foundation is built on the principle that true intelligence emerges from physical interactions, state navigation, and causal relationships.
Association Strength Theory (AST)
At the core of our Physical AI approach is the Association Strength Theory, which provides a mathematical and conceptual framework for understanding how intelligent systems
can navigate through complex state spaces while maintaining interpretability and predictability. AST enables machines to form meaningful associations between states,
actions, and outcomes in both physical and abstract spaces.
Unlike traditional machine learning approaches that rely heavily on pattern recognition in large datasets, Physical AI focuses on understanding the underlying causal mechanisms
that govern how systems transition between states. This approach provides several key advantages: interpretability, robustness, and the ability to generalize beyond training data.
State Navigation Framework
Our state navigation framework enables AI systems to understand and navigate through complex spaces, whether they are physical environments, decision spaces, or abstract
conceptual spaces. This framework provides the foundation for autonomous decision-making, robotic navigation, and intelligent content generation.
Autonomous Intelligent Beings
Intelligent systems and applications/features of interest in the industry and people's life comprises:
control systems, autonomous systems, autonomous moving systems, decision making, content generation,
question answering, knowledge discovery, and investigation of bodies of knowledge/data. These features, applications and systems are all modeled, architectured,
and implemented as systems comprising at least one state navigator, in which the systems will respond
in various forms in response of an input query or set of data wherein a machine or a system changes its
state from a current state to a next or a future state. Methods are given to enable the systems to navigate
through spaces either physical spaces and/or a state space.
Learn more
Autonomous Systems
Decision Making
Knowledge Discovery
State Navigation
The foundation of Physical AI is built on decades of research into how intelligent systems can operate in the real world. By grounding AI in physical reality and causal relationships,
we create systems that are not only more capable but also more trustworthy and aligned with human understanding of how the world works.
Core Principles
The fundamental principles that guide our Physical AI research and development
Physical Grounding
AI systems that operate within the constraints and opportunities of the real physical world, not just digital spaces.
Causal Understanding
Systems that understand cause-and-effect relationships rather than just statistical correlations.
State Navigation
The ability to navigate through complex state spaces with interpretable and predictable behavior.
Research Areas
Key research domains that contribute to our Physical AI foundation
Knowledge Representation
Developing novel approaches to representing knowledge in ways that enable physical AI systems to understand and reason about the world.
• Semantic state representations
• Causal knowledge graphs
• Physical constraint modeling
Autonomous Systems
Creating autonomous systems that can operate independently while maintaining safety, reliability, and interpretability.
• Decision-making frameworks
• Safety and verification
• Human-AI collaboration
Robotic Intelligence
Advancing robotic systems that can understand, navigate, and interact with the physical world intelligently.
• Physical interaction modeling
• Spatial reasoning
• Multi-modal perception
Content Generation
Developing AI systems that can generate meaningful, contextually appropriate content based on physical understanding.
• Context-aware generation
• Physical constraint integration
• Multi-modal content creation
Explore the Foundation of Physical AI
Discover how our foundational research is shaping the future of artificial intelligence and autonomous systems.
