Train robots inthe world they'llactually live in.
Kyros provides physics-accurate simulation environments where robotics teams train AI models at scale, before a single real-world deployment.
Robots learn the hard way.
Until now.
Real-world training
- Weeks of setup per task
- Robot damage and safety risk
- Impossible to scale variations
- No edge case coverage
Kyros simulation
- Environments ready in minutes
- Zero hardware risk
- 10,000+ procedural variations
- Full edge case library
From prompt to policy.
Three steps. Zero hardware required.
Choose an Environment
Browse our marketplace of pre-built, physics-accurate simulation environments. Kitchen, warehouse, outdoor terrain, hospital room. Every major task category is covered.
genesis_poweredConfigure Your Training
Select a task from our library, set your domain randomization parameters, choose your curriculum difficulty progression. No environment-building required.
10,000+ variationsDeploy to Real Hardware
Export your trained policy. Our transfer-optimized environments are validated for real-world deployment. What trains in Kyros, transfers to your robot.
zero-shot transfer# Kyros Platform SDK — v1.0import kyros# Load a pre-built kitchen environmentenv = kyros.environments.load("kitchen_v4.2", robot="franka_panda", variations=10000, domain_randomization=True)# Configure training curriculumcurriculum = kyros.curriculum.build( task="pick_and_place", difficulty="progressive", edge_cases=["clutter", "occlusion", "low_light"])# Train policy — runs 430,000x faster than real timepolicy = kyros.train(env, curriculum, timesteps=1e8)# Export for real-world deploymentpolicy.export("my_robot_policy.pt")# Ready to deploy. Zero real-world training required.Everything your sim-to-real pipeline needs.
Environment Marketplace
Hundreds of pre-built, task-specific environments. Kitchens, warehouses, outdoor terrain, hospitals. Ready in minutes, not months.
500+ environmentsTask Libraries
Pre-configured task definitions with reward functions, success criteria, and observation spaces. Don't write reward code. Just train.
pick, place, grasp, fold...Training Curricula
Ordered difficulty progressions proven to produce generalizable policies. Start easy, end expert. Built by researchers who've done it before.
progressive difficultyDomain Randomization
10,000 object variations per environment. Tested randomization configs that maximize transfer rate without breaking policy learning.
sim-to-real optimizedGenesis-Powered Physics
Built on Genesis — the physics engine that runs 430,000x faster than real time. Train policies in 26 seconds that used to take days.
430,000x speedupVersion Control for Envs
Track environment versions, compare policy performance across variants, roll back to previous configs. Git for simulation.
environment versioningCommunity Contributions
Share environments with the Kyros community. Earn credits when others use your work. The more the community builds, the better everyone trains.
open marketplaceTransfer Validation
Every Kyros environment is validated against real-hardware transfer benchmarks. Verified environments carry a 'Transfer-Tested' badge.
transfer-verifiedSee the training worlds we've built.
What researchers say about the problem we're solving.
The sim-to-real gap remains a major obstacle. Agents trained in simulators often struggle to maintain performance when transferred to real-world physical environments.
Creating realistic physical simulations requires extensive technical expertise, expensive hardware, and time-consuming manual processes. Existing tools often fail to deliver the speed, accuracy, and user-friendliness needed.
The robotics and embodied AI field has long struggled with accessibility and efficiency issues — making robotics research an exclusive domain for well-funded institutions.
The robots of tomorrow need training grounds today.
Join the waitlist. Be the first team to access Kyros.