Building World-Aware Physical AI with Agent Memory and Context Graphs

What if robots didn’t just react - but actually understood their world and remembered it? In this session, we explore how to build world-aware, context-driven agents using a graph-based memory layer and real-world robotics. You’ll see how users, objects, and environments can be modeled as a living digital twin, how interactions are captured as structured memory, and how agents reason over long-term context. Through a live demo, a robot recognizes returning users, recalls preferences, and adapts its behavior across sessions. This talk provides a practical blueprint for building persistent, embodied AI systems that move beyond stateless interactions.