Six synchronized data layers. Zero manual annotation.
Drop into any robot gym, sim framework, or training pipeline.
Purpose-built for teams training robots in indoor spaces. Every environment is constraint-generated — not stitched from scans — so room connectivity, obstacle volumes, and surface heights are exact and queryable by design. No reconstruction. No cleaning. No wait.
One configuration. Unlimited structurally distinct buildings. Each with all six data layers ready — no post-processing, no annotation step.
The constraint engine runs over 1,000,000 validated architectural rules — room adjacency, circulation logic, door placement, structural limits. Not random: deterministic diversity. Every output is unique and valid.
Walls, roof, furniture, semantic labels, and topology are all written from a single JSON knowledge model. USD, IFC, GLB, and JSON are generated natively — not converted from each other.
8 configurations. 8 structurally distinct buildings. Unique room arrangements, door placements, and window counts — all within the same parameter set. All architecturally valid.
Working proofs-of-concept — not renders. Each uses the topology graph, semantic layer, and furniture bounding boxes directly from the dataset. No preprocessing required.
Adjust rooms and common areas. The constraint engine resolves adjacency, circulation, and door logic in real time. Every configuration produces a unique, valid building — not a random one.
Click any layer to inspect real output from building plan_4341973. Geometry, semantics, topology, navigation, furniture, and output formats are all derived from a single JSON knowledge model.
Scan-based and existing procedural datasets were designed for visual navigation benchmarks. They produce geometry and images — not the structured spatial knowledge required for task execution. Here's where that gap shows.
| Capability | Scan-based | Procedural | Indoor Datasets |
|---|
Most existing indoor datasets were built for visual navigation benchmarks — they provide geometry and images, but not the structured spatial knowledge that robot task execution requires. Indoor Datasets is purpose-built for structured robot training: every environment ships with explicit connectivity, semantic labels, surface heights, obstacle volumes, and multi-format export — all generated without manual annotation.
Tell us your robot training setup. We'll prepare a complete 6-layer dataset sample matched to your use case — and walk you through it.