01
Real-world calibration layer
Passively ingests deployment telemetry and compares expected versus observed outcomes across environments, tasks, and robot types.
Simulation-to-real transfer for robotics teams
Make robot models work outside the simulator.
Sim2Real helps robotics teams detect why simulation-trained models fail in real environments, then turns those failures into better simulation conditions, stronger training data, and more reliable deployment performance.
By creating an account or requesting access, you agree to the Terms of Service and Privacy Policy. Paid subscriptions renew automatically until canceled.
See Sim2Real in action
Built for pilot deployments, warehouse automation, manufacturing workflows, and real-world robotics validation.
Robotics startups
Warehouse operators
Manufacturing teams
Enterprise pilot programs
The Sim-to-Real Gap
The simulator is not your deployment environment.
Most robotics models fail in production for reasons that do not show up in clean synthetic training data. Lighting shifts, worn surfaces, box tilt, clutter, friction variance, dust, and sensor noise create failure patterns that standard simulation workflows miss.
Teams often respond by collecting more real-world data manually, but that process is slow, expensive, and difficult to scale across changing environments. Sim2Real closes that gap by learning directly from deployment failures and using them to improve future simulation runs.
Shorter
pilot iteration loops when failure causes are surfaced quickly
Lower
manual data collection overhead across changing environments
Clearer
evidence for deployment readiness across tasks, sites, and fleets
Product Workflow
A feedback loop from the real world back into training
Sim2Real captures what actually happened during robot execution, compares it with what the simulator expected, and generates better training conditions for the next run.
1
Capture deployment telemetry
Collect camera, force-torque, IMU, and task outcome data from real robot operations.
2
Detect failure causes
Identify where simulation assumptions diverged from actual conditions, including geometry variance, friction, clutter, and contact behavior.
3
Improve future training
Generate updated simulation parameter sets and synthetic scenarios that better mirror deployment conditions.
4
Redeploy with evidence
Track whether transfer reliability improves over time and use the dashboard to prioritize the next calibration pass.
Core Product Capabilities
Built to turn failures into training signal
Each module is designed for robotics teams that need clear operational outcomes, not generic AI dashboards.
02
Failure analysis engine
Classifies failure patterns and surfaces repeatable sim-to-real gaps by task, environment, object type, and severity.
03
Simulation perturbation generator
Transforms observed discrepancies into updated simulation conditions including friction, clutter density, lighting variance, and pose randomness.
04
AI reality translator
Uses structured scene interpretation to describe deployment conditions in simulator-ready terms without requiring new hardware.
05
Closed feedback loop
Turns failed attempts into better synthetic training data so each deployment cycle improves future model robustness.
06
Dashboard and deployment insights
Shows failure trends, transfer bottlenecks, simulator recommendations, and readiness signals across pilot and production programs.
Use Cases
Built for real deployment conditions
From warehouse handling to variable manufacturing lines, Sim2Real is framed for operators who care about uptime and repeatability.
Reduce grasp and handling failures in messy environments
Calibrate simulation assumptions against actual clutter, lighting shifts, and object variance before pilot issues compound.
Improve reliability in repetitive but variable workflows
Catch the small physical differences that create expensive downtime in production cells and repetitive tasks.
Move faster from pilot to production
Learn from deployment data instead of rebuilding workflows around repeated field failures and expensive re-collection cycles.
Standardize transfer evidence across sites
Use shared metrics, reporting, and recommendation flows to compare readiness across programs and facilities.
Operational Value
Reduce the cost of failed pilots
When sim-to-real gaps remain hidden, robotics teams lose time in debugging, relabeling, repeated retraining, and on-site delays. Sim2Real gives teams a systematic way to identify what is breaking, why it is breaking, and how to adjust training conditions before failure patterns multiply.
Shorten pilot iteration cycles
Prioritize the highest-impact calibration updates first.
Improve model robustness
Stress-test against realistic perturbations instead of clean synthetic averages.
Reduce manual data collection
Turn existing deployment telemetry into more useful training signal.
Surface hidden transfer bottlenecks
See which conditions keep breaking performance before rollout expands.
Pricing Preview
Pricing built for pilots and production fleets
Start with a compact pilot plan or move directly into enterprise transfer workflows with onboarding and custom integrations.
Pilot Optimizer
$499
per month
- Up to 3 robots
- Core failure analytics
- Baseline simulation recommendations
- Weekly reporting
- Email support
Enterprise Transfer
$2,500
starting per month
- Multi-robot support
- Advanced simulation generation
- Custom integrations
- Priority support
- Dedicated onboarding
Prices are listed in USD unless otherwise noted. Applicable taxes may be added at checkout. Paid plans renew automatically until canceled. Full plan details live on the pricing page.
FAQ
Questions buyers ask before rollout
What problem does Sim2Real solve?
Sim2Real helps robotics teams close the gap between simulation-trained performance and real-world deployment behavior.
Does Sim2Real replace my simulator?
No. It works alongside existing simulation and robotics stacks to improve the realism and usefulness of your training loop.
What integrations do you support?
Support can include ROS, Isaac Sim, Omniverse, MuJoCo, and custom APIs depending on plan level and deployment scope.
How is billing managed?
Paid subscriptions are managed through secure billing workflows, with subscription changes, payment method updates, invoices, and cancellations available through a customer billing portal.
Can I cancel anytime?
Customers can manage subscriptions according to plan terms, including cancellation through the billing portal or account settings where available.
Final CTA
Make deployment failures useful.
See where your simulation assumptions break, then feed those insights back into training before they slow down your rollout.