Grounding Agentic AI in the Real World

The AI paradigm is shifting from models that simply know information to Agents that act directly. Now, foundation models (FMs) serve as the core engine of Physical AI, planning and using tools in physical environments such as warehouses, factories, and hospitals.

Agentic AI like Amazon's Project Eluna analyzes real-time data to improve operational efficiency, but Hallucination occurring in physical environments can lead to fatal accidents. For example, a robot path proposal that ignores an object's inertia or mass could cause casualties or equipment damage.

To address this, Grounding technology—which integrates external information, physical laws, and numerical simulation to align the model's reasoning with reality—is essential. The four key approaches for this are as follows.

• Physics-Guided Deep Learning (PGDL): Integrates physical principles such as mass and the law of conservation of energy into model training to improve data efficiency and ensure compliance with physical laws.
• Uncertainty-Aware Reasoning (UQ4CT): The model measures its own uncertainty, and stops operation or requests human intervention if it exceeds a safety threshold.
• Adaptation-While-Learning (AWL): Extracts knowledge from physics simulators and dynamically calls specialized tools to bridge the gap between text and numerical data.
• Verifier-augmented grounding: Uses external software to verify that the model's reasoning does not deviate from the bounds of logic and reality.