Simulation assets for
agritech
Farm robotics, weeding, spraying, harvesting, autonomous tractors, need simulation environments with accurate crops, soil, implements, and equipment. Rigyd converts agricultural 3D models, images, or text into physics-enabled SimReady assets.
The problem
Why existing workflows fall short.
Biological variability is enormous
Every plant is unique: leaf shape, stem stiffness, fruit mass, ripeness stage. Capturing this variability for perception and manipulation training is effectively impossible by hand.
Outdoor physics demands varied contacts
Crops deform, branches bend, soil compresses, tools cut through material. Rigid-body approximations with default values miss how farm robots actually interact with biological materials.
Season-to-season asset reuse is low
A strawberry harvest model doesn't transfer to an apple orchard or a corn field. Each crop, each growth stage, and each implement needs its own physically accurate asset set.
How Rigyd helps
AI-native infrastructure that automates the hard parts.
Crop and plant asset conversion
Convert 3D plant scans and crop models into physics-enabled assets with realistic mass and collision geometry, calibrated against biological material density and surface properties.
Farm equipment physics
Accurate mass and inertia estimation for tractors, implements, trailers, and tools, essential for autonomous tractor navigation and implement-coordinated training scenarios.
Material-based friction
Soil, foliage, fruit skin, and machinery surfaces each have distinct friction coefficients. Rigyd's material identification produces calibrated per-region values for every asset.
faster than manual crop and equipment asset preparation
better real-world performance with physically accurate data
unique plant and equipment variants practical per season
Simulate your farm robotics at crop scale
Generate agricultural SimReady assets from 3D models, images, or text descriptions for farm robot training.
Frequently asked questions
Can Rigyd generate physics for biological materials like crops?
Yes. Material identification distinguishes foliage, fruit skin, stems, and soil, assigning calibrated friction and mass values per region. Results are tuned for robotic interaction with biological materials.
Does Rigyd handle farm equipment like tractors and implements?
Yes. Rigid-body physics estimation covers tractors, trailers, implements, and hand tools, with accurate mass and inertia for autonomous tractor navigation and implement-coordinated simulations.
Can I build a season-specific simulation library?
Yes. Rigyd's bulk processing makes it practical to generate 1,000+ crop and equipment variants per growing season, covering ripeness stages, crop types, and equipment configurations.
Does Rigyd support GIS data for terrain and field layouts?
Rigyd processes 3D mesh geometry (terrain heightmaps converted to mesh, field boundaries as USD prims). Direct GIS format ingestion (shapefile, GeoTIFF) isn't supported, convert through a GIS-to-3D pipeline first. Once converted, Rigyd handles per-region material identification for soil types, crop patches, and field boundaries, so terrain physics (soil compression, friction) reflects real-world variation across a farm.
Can Rigyd model plant growth stages or fruit ripeness?
Yes, via USD variants. A crop asset can carry multiple geometry variants (seedling → mature plant → harvest-ready) with per-stage physics overrides (plant stiffness, fruit mass, branch deformability under load). Switching variants in simulation is a runtime selection, so the same scene can sweep across the growing season without reloading assets. Useful for harvesting-policy training across crop maturity ranges.
Related reading
In-depth guides on robotics simulation, OpenUSD, and SimReady assets.
15 JUL 2026
Automatically Estimating Physics Properties for Simulation Assets
Mass, friction, and collision geometry are what make a 3D model behave in a physics engine. This is how those properties get estimated automatically, and why automating them is what lets teams build simulation assets at scale.
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.
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