Rigyd
Simulation-ready 3D assets for robotics simulation, at scale
Rigyd launched on May 21, 2026 with 59 votes, #719 of 20,538 launches that month and more than 95% of that year's launches.
What it does
Robot learning is only as good as its data. For sim-first teams, success depends on domain randomization. Train a humanoid in 1M simulated kitchens so it doesn't fail in the real one. Or train a robotic arm on thousands of objects of varying sizes, masses, and materials. Both require physics-accurate 3D content at scale. Most teams don't have it. Rigyd auto-generates and randomizes physics-accurate 3D objects and worlds at scale, unlocking sim-to-real scenarios that were previously impossible.
Generate physically accurate SimReady objects and worlds from raw 3D models, images, or text. OpenUSD and MJCF, ready for Isaac Sim, MuJoCo, and Genesis.
Turn an image or text prompt into millions of simulated kitchen, warehouse, and factory objects, instead of spending weeks modeling and tuning them by hand. Prompt or image in, simulation-ready asset out, in minutes. Generated for your use case, not pulled from a library. Physics derived, never guessed. Drop and settle tested, auto-corrected, every SimReady check before export. OpenUSD and MJCF with physics intact. Loads directly into Isaac Sim, Isaac Lab, and MuJoCo. Collision detail where the robot makes contact, simplified everywhere else. Triangle and primitive counts included. It researches the object and picks the right method for every part: visual fidelity where it shows, physics…from rigyd.com
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Physics-accurate 3D assets for robotics simulations from any inputApr 2026 · app.rigyd.com · ▲8In the past 4 years, we developed a platform generating 3D visuals and deploying AR Try-on experiences into large footwear and retail companies. We focused on high-fidelity meshes and textures since the point was giving shoppers a realistic visual. Then robotics companies started to approach us and they asked for thousands of 3D assets that they can use in creating different simulation environments. This is crucial for them so they can achieve better sim-to-real transfer with domain randomization (train a humanoid in a million different kitchens so a real one is not a surprise when it is…
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