FORMAL KNOWLEDGE
Ontology is about meaning, not just field names.
IEEE 1872 introduced a core ontology for robotics and automation as a formal reference vocabulary for knowledge representation and reasoning. The surrounding IEEE 1872 family extends this direction into autonomous robotics and robot task representation.
The goal is not to replace robot-description files. An ontology supplies a shared conceptual layer: what is a robot, a component, a task, an environment, a capability or a relation, and how those concepts connect.
WHY IT MATTERS
Heterogeneous robots need more than compatible syntax.
Shared vocabulary
Different systems can use consistent concepts instead of vendor-specific labels.
Reasoning
Formal relations can support inference over robot, task and environment knowledge.
Autonomy
Autonomous-system concepts can be represented independently from one hardware vendor.
Tasks
Task knowledge can be structured and communicated across planners and system layers.
Multi-robot
Shared semantics become increasingly important when multiple robots coordinate.
Interoperability
Semantic agreement can complement transport, middleware and file-format compatibility.
DESCRIPTION → SEMANTICS
A structural model can feed an ontology without becoming one.
KnowRob’s Semantic Robot Description Language is an important precedent: it imports robot hardware structure from URDF, annotates it with semantic information and adds representations for software and capabilities so the system can reason about configuration.
OPENRGD PERSPECTIVE
OpenRGD should align with ontology work, not pretend it invented semantics.
OpenRGD’s opportunity is practical: connect evidence-backed embodiment data, operational constraints, capabilities and provenance to modern Physical AI and Embodied AI workflows. Existing IEEE and KnowRob work should be treated as reference context and potential interoperability targets.