Digital twin assets for
Unreal Engine
Create physics-enabled assets for Unreal Engine robotics simulations and digital twins. Automatic physics estimation, collision geometry, and OpenUSD output.
The problem
Why existing workflows fall short.
Physics setup is fragmented
Unreal Engine requires separate physics asset configuration, collision setup, and material assignments. Each asset needs individual attention.
Digital twins need thousands of objects
Factory and warehouse digital twins require massive libraries of physically accurate objects. Manual creation can't keep up.
Sim-to-real accuracy gap
Inaccurate physics parameters in UE simulations lead to policies that fail in the real world. Getting physics right is critical.
How Rigyd helps
AI-native infrastructure that automates the hard parts.
OpenUSD bridge to UE5
Rigyd outputs OpenUSD that imports directly into Unreal Engine 5 via the USD Stage Actor, maintaining physics properties.
Automated collision complexity
Convex decomposition generates collision geometry at configurable complexity, simple hulls for navigation, detailed meshes for manipulation.
Material-based physics
AI identifies materials from mesh geometry and appearance, then assigns calibrated friction, restitution, and density values.
cost reduction in digital twin asset creation
per asset, from upload to SimReady
better sim-to-real transfer with accurate physics
Build physically accurate Unreal Engine simulations
Convert 3D models, images, or text into simulation-ready assets for UE5 in minutes.
Frequently asked questions
How do Rigyd assets import into Unreal Engine 5?
Rigyd outputs OpenUSD that imports via UE5's USD Stage Actor. Mass, collision geometry, and material physics are applied automatically, no per-asset Physics Asset configuration required.
Can Rigyd handle digital twin scale for UE5?
Yes. Enterprise API has converted multi-thousand-object catalogs for factory and warehouse digital twins. Each asset is physics-validated, so the twin simulates operations, not just renders geometry.
Does Rigyd support UE5 Chaos Physics?
Yes. OpenUSD physics properties map to UE5's Chaos solver on import. Collision complexity is configurable, simple hulls for navigation, detailed convex decomposition for manipulation and contact-rich tasks.
How do Rigyd assets perform with Nanite and Lumen in UE5?
Visual meshes from Rigyd are Nanite-compatible, triangle counts are preserved from the source, so high-poly assets stream efficiently through Nanite's virtualized geometry. Collision meshes are kept as separate, low-poly convex decompositions, so Chaos contact detection stays fast while Nanite handles visual rendering. Lumen indirect lighting works against either visual mesh.
Can Rigyd populate UE5 Physics Asset settings automatically?
Yes. On USD import to UE5, Rigyd's physics metadata populates Static Mesh Component's simulation flags, collision presets, and physics material assignment. For articulated mechanisms, joint constraints and drive parameters auto-populate the Physics Asset Editor, eliminating per-link manual setup that's otherwise the slowest part of bringing a robot into Unreal. The same applies to Unreal's NVIDIA PhysX-based legacy projects: Rigyd's assets carry both Chaos- and PhysX-compatible attributes so the migration path between solvers stays open, useful for studios still on UE 5.0-5.2 evaluating when to switch.
Related reading
In-depth guides on robotics simulation, OpenUSD, and SimReady assets.
9 JUL 2026
Scaling Simulation Asset Libraries Beyond Curated Inventory
Curated SimReady libraries are a great starting point but a hard ceiling. Here is why fixed inventories limit robotics simulation at scale, and how on-demand asset generation closes the gap.
2 JUL 2026
Domain Randomization for Robotics Training: Asset Diversity at Scale
Domain randomization makes trained policies transfer to the real world, but it only works if your asset library is diverse enough. Here is how asset diversity drives randomization, and how to generate it at the scale training needs.
30 JUN 2026
Building a Scalable Embodied AI Asset Pipeline: From Raw Data to Simulation
A practical look at the stages of an embodied AI asset pipeline, from raw 3D data and reference inputs to physics-ready simulation assets, and what changes when you need to produce thousands of them instead of a handful.
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