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.
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.
Updated 2026-05-08
The problem
Why existing workflows fall short.
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
Automate 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.
40%
better real-world transfer with physically accurate RL training
1,000+
unique objects practical for RL datasets
97%
faster than manual physics annotation
FAQ
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.
Train RL policies on realistic environments
Bring 3D models, images or text and get validated OpenUSD and MJCF, with collision geometry optimised for RL throughput.