SDF VS RGD

Simulation description and semantic embodiment are different responsibilities.

SDFormat can describe robots, models, sensors, physics and entire simulated worlds. RGD focuses on the semantic identity and embodied context of the robot itself across physical, operational, capability and lifecycle domains.

CITATION RECORDCanonical source ↗
MATURITYREFERENCE COMPARISON
LAST VERIFIED22 Sep 2026
SOURCE VERSIONSDFormat specification + OpenRGD architecture
EVIDENCE BOUNDARYComplementary-layer comparison; OpenRGD does not replace SDF worlds, simulator configuration or plugins.

RESPONSIBILITY MATRIX

Different centers of gravity.

DIMENSIONSDFORMATRGD
Primary responsibility

Simulation-oriented robot, model and world description.

Semantic embodiment and AI/runtime grounding context.

Core representation

XML models, links, joints, sensors, physics, plugins and worlds.

Modular graph/profile across seven semantic domains.

World description

First-class: models, scene, lights, physics and environment.

Not a world/simulator format.

Sensors / physics

Rich simulator-oriented declarations.

Can record embodiment evidence and constraints, not simulate them itself.

Robot identity

Model identity exists inside simulation description.

Canonical robot/profile identity and integrity are explicit.

Capabilities

May be represented indirectly through plugins/stack-specific semantics.

Explicit capability and action semantics belong to Agency.

Lifecycle

Not the primary focus of the format.

Evolution domain represents wear, adaptation and continuity.

Actuation

Simulation configuration is not real hardware authorization.

RGD context is likewise non-actuating.

OVERLAP

Where both may describe the same robot facts.

  • Robot model structure, joints and physical properties.
  • Sensor declarations can inform embodiment context.
  • Simulation constraints may correspond to real operational limits when provenance is known.
  • Both may describe the same robot at different layers of the stack.
  • Both benefit from clear version and source ownership.

WHAT RGD ADDS

Context beyond the source description.

  • Cross-environment identity independent of one simulator world.
  • Explicit provenance for physical facts and enrichment.
  • Operational and safety context outside simulator-specific configuration.
  • Capability and action semantics for AI systems.
  • Lifecycle and continuity across physical changes.
  • Execution-boundary semantics between planning and hardware.

RGD DOES NOT REPLACE

Keep ownership explicit.

  • SDF world composition.
  • Simulation physics settings and environment configuration.
  • Simulator-specific plugins and sensor implementations.
  • Gazebo or another simulator runtime.
  • Middleware, controllers or real device drivers.

USE BOTH WHEN

Complementary layers are stronger than forced convergence.

  • SDF is already the source of simulated robot/world configuration.
  • The same embodiment must be represented consistently outside the simulator.
  • AI systems need capabilities and constraints not encoded as simulator plugins.
  • You want simulation assets and semantic identity to evolve independently.
  • Provenance needs to distinguish simulated assumptions from verified physical evidence.

OPENRGD FLOW

Source evidence can become semantic context without erasing provenance.

01SDF model/world
02Evidence adapter
03Physical/sensor evidence
04RGD enrichment
05Validated embodiment context
06Simulation + physical runtime

PRIMARY SOURCES

← All description formatsRGD reference →