Robotic Reasoning Models for Physical AI

RRM-1 brings together perception inputs, a persistent symbolic world model, AI reasoning, explicit verb and action semantics, safety checks, and an embodiment adapter. The world model represents the system’s evolving beliefs about objects and their relationships, giving higher-level reasoning a persistent context for planning and checking the effects of actions.

The architecture uses defined action preconditions and expected effects, with separate checks on symbolic actions and proposed motion. An embodiment adapter maps higher-level actions to a particular robot’s capabilities and execution interface. The goal is to keep reasoning and world modeling from being tightly coupled to one robot, AI model, sensor stack, or embodiment; transfer across robots remains a goal to evaluate.

The framework and its evaluation are still developing.

This is an intentionally high-level public overview. The implementation, experimental setup, and current results remain private while the work develops.