A
Perception mismatch
Flag failures caused by lighting shifts, occlusion, glare, clutter, texture changes, or unexpected object appearance.
Product Architecture
See how Sim2Real turns deployment evidence into better training data.
Sim2Real captures what happened in the field, identifies where simulator assumptions broke, and creates updated synthetic conditions for the next training pass.
Data Capture
How deployment telemetry is captured
Sim2Real is designed to ingest the evidence teams already have: camera feeds, force-torque traces, IMU signals, mission metadata, and outcome tags from real robot execution.
Telemetry inputs
- RGB and depth camera observations
- Force-torque and tactile measurements
- Joint state, kinematics, and controller signals
- Task outcomes, failure flags, and operator annotations
Why it matters
Robotics teams often know that a deployment failed, but not which part of the simulation model stopped matching reality. Sim2Real makes those mismatches legible enough to act on.
Failure Analysis
How failures are classified
The failure analysis engine groups errors by physical cause so the training loop can update the right assumptions instead of treating every failure as generic noise.
B
Physics mismatch
Track failures rooted in friction, contact behavior, inertial differences, compliance, or surface wear that were not represented well in simulation.
C
Task mismatch
Separate policy, sequencing, or edge-case task errors from perception and environment issues so teams can target the right next action.
Synthetic Retraining
How simulation parameters are updated
Once discrepancies are identified, Sim2Real converts them into simulator-ready parameter ranges, perturbation sets, and retraining batches for the next iteration.
1
Map observed discrepancies
Translate real scenes into structured changes such as friction, lighting, clutter density, object pose, and sensor noise.
2
Generate perturbation sets
Create focused simulator scenarios that stress the exact regions where policies failed in deployment.
3
Assemble synthetic retraining batches
Prepare new scenario combinations that can be routed into your existing training workflow.
4
Measure improvement
Compare readiness, failure rate, and repeatability over time to see whether transfer quality is improving.
Integrations
Supported robotics stack integrations
Sim2Real is positioned to work alongside the tooling robotics teams already depend on for simulation, orchestration, and policy evaluation.
ROS / ROS2
Isaac Sim
Omniverse
MuJoCo
Custom APIs
Telemetry pipelines
Operational Outcome
Why the product reduces failure rates and pilot delays
Without a structured sim-to-real loop, teams collect more data than they need, retrain more broadly than necessary, and burn time onsite figuring out why a robot did not behave as expected. Sim2Real narrows that loop by identifying the most meaningful differences between synthetic assumptions and deployed reality.
Lower failure rates
Teams can focus on the scenarios most likely to break performance in production.
Fewer pilot delays
Deployment evidence turns into a structured backlog of calibration work instead of a vague debugging spiral.
Less real-world data overhead
Existing telemetry becomes more valuable because it feeds better synthetic retraining.
Cleaner executive reporting
Decision makers get repeatable signals around transfer quality and readiness.
Next Step
See how it fits your deployment workflow.
Book a demo to walk through telemetry ingestion, failure classification, retraining workflow design, and deployment reporting.