RL training
environments
Reinforcement learning for robotics needs massive environment diversity and realistic physics, especially for sim-to-real transfer. Rigyd converts 3D models, images, or text into SimReady assets calibrated for RL pipelines.
The problem
Why existing workflows fall short.
RL demands millions of training steps
Reinforcement learning runs for millions of episodes. Every step whose physics deviates from reality compounds into a larger sim-to-real gap by the time the policy finishes training.
Environment diversity drives generalization
Policies trained on a narrow asset set overfit to specific geometries and fail on new objects. Diversity, not algorithmic tricks, is what produces robust policies in the real world.
Asset creation bottlenecks research
Research teams spend more time preparing assets than training policies. Every hour on collision-mesh generation or physics tuning is an hour not spent on algorithms or evaluation.
How Rigyd helps
AI-native infrastructure that automates the hard parts.
SimReady for Isaac Lab, Brax, MuJoCo
Rigyd outputs work with the major RL simulation stacks, Isaac Lab, Brax (MJX), MuJoCo, Gazebo. Physics properties survive format conversion, so training pipelines stay consistent.
Domain-randomization-ready physics
Mass within 15-20% and friction within 0.1, inside typical DR variance ranges, so RL policies can center randomization on realistic baselines instead of arbitrary guesses.
Scale object libraries for free
Convert thousands of unique objects with one bulk operation. Diverse training sets are no longer gated by asset-creation budgets or graduate-student weekends.
better real-world transfer with physically accurate RL training
unique objects practical for RL datasets
faster than manual physics annotation
Train RL policies on realistic environments
Bring 3D models, images, or text and get SimReady assets calibrated for RL pipelines.
Frequently asked questions
Does Rigyd work with Isaac Lab and MJX?
Yes. Rigyd emits OpenUSD and MJCF so assets run in Isaac Lab, MuJoCo, MJX, and Brax without modification. Physics properties survive format conversion across RL simulation stacks.
How does physics accuracy affect RL training?
RL compounds small errors over millions of training steps. Physics within 15-20% of measured (vs. arbitrary values) produces policies that transfer to real hardware ~40% better, per NVIDIA GR00T N1 benchmarks.
Can I build diverse training object sets at RL scale?
Yes. Bulk conversion makes thousands of unique objects practical, the environment diversity that drives policy generalization. Previously bottlenecked by hand-annotation; now one API call.
Does Rigyd integrate with Isaac Lab and Brax RL training pipelines?
Yes. Isaac Lab consumes USDPhysics assets natively via Isaac Sim. Brax (and MJX) consumes MJCF, which Rigyd exports on-demand from the same OpenUSD source. Asset properties (mass, friction, inertia, joint drives) survive both pipelines, so an RL policy can be trained in Isaac Lab and evaluated against Brax, or vice versa, with the same physical primitives in both runtimes.
How many unique objects should an RL training scene contain for robust transfer?
For manipulation policies, 1,000-5,000 unique objects with proper domain randomization is the typical sweet spot. Below 500, policies overfit to specific geometries. Above 10,000, returns diminish, diversity already saturates the policy's generalization capacity. Locomotion and navigation policies need fewer unique objects (100-500) but more environment variation. Rigyd's bulk pipeline makes either scale practical.
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.
Explore more
SimReady for Isaac Sim
OpenUSD assets validated for NVIDIA Isaac Sim
Models for MuJoCo
Physically accurate assets for robot learning
Assets for Gazebo
Simulation-ready models for ROS 2
Warehouse Simulation
50,000+ SKUs need physics properties
Humanoid Robots
Whole-body interaction training assets
Sim-to-Real Transfer
Close the gap with accurate physics data