Measure sim-to-real performance with two separate scorecards: how well a transferred policy performs on the real robot, and how well simulation predicts differences in real-world performance across policies or conditions. Report the robot, task, trial conditions, task metrics and failures alongside both scores. A single “sim-to-real gap” number cannot answer all of those questions.
What does a sim-to-real evaluation need to measure?
There are two distinct questions. The first is operational: does the policy accomplish the task on the physical robot, and how well? The second is predictive: when simulation scores two or more policies or conditions, do those scores track the outcomes measured on hardware?
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The 2026 Annual Review survey The Reality Gap in Robotics: Challenges, Solutions, and Best Practices distinguishes metrics for transfer performance from metrics for the reality gap. Keeping the questions separate prevents a strong result on one from being mistaken for evidence of the other.
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Transfer performance: the policy on hardware
For a task with a clear pass/fail definition, report real-world task success rate over repeated trials. State exactly what counts as success. Pair that rate with a continuous, task-specific measure when it helps show partial progress or explain failures: for example, time to goal or path efficiency for navigation, or object distance to its target for manipulation.
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Cumulative reward can add detail for reinforcement-learning tasks, but only when the reward definition is interpretable and consistent across simulation and hardware. Reward totals based on unlike definitions are not meaningfully comparable just because they share a name.
Predictive validity: simulation as a comparison tool
To test whether simulation predicts real-world performance, evaluate multiple policies or method-task conditions in both domains, then compare their scores. The Annual Review survey describes the sim-to-real correlation coefficient (SRCC) using Pearson correlation between simulated and real task performance. Report the paired results or scatter plot as well as the correlation when possible: an overall coefficient can obscure outliers and does not say whether real-world performance is acceptable in absolute terms.
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Correlation indicates whether scores tend to move together; it is not the same as policy success, nor does it establish that simulated and real scores have the same scale. A single policy evaluated in both domains can establish a transfer result for that policy and setup, but cannot establish whether simulation reliably ranks alternatives.
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Which metrics should you report?
| Evaluation question | Useful measure | What to report with it |
|---|---|---|
| Did the policy complete the real task? | Real-world success rate | Success definition, repeated-trial protocol and the tested robot and task |
| How well did it perform, including partial progress? | Task-specific measure, such as time to goal, path efficiency or object distance to target | Units, measurement method and whether the score is from simulation or hardware |
| Does simulation track real outcomes across alternatives? | Correlation across paired policy or condition scores; the Annual Review survey describes SRCC using Pearson correlation | Paired scores, policies or conditions included, and absolute real-world outcomes |
| How does an RL policy behave over a task? | Cumulative reward, if reward definitions are consistent and interpretable | Reward definition and any difference between the simulated and real task |
No single task measure fits every robot. Select metrics that reflect the task’s objective and failure modes, and do not treat unlike reward definitions or task measures as if they shared a common scale. The Annual Review survey cautions that aggregate success can hide different failures; record failure categories and safety-relevant outcomes rather than reporting only an average.
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How should you design the evaluation?
- Define the task and success criterion. Specify the pass/fail rule before testing. Add a continuous measure if success rate alone would make partial progress or the severity of a failure hard to see.
- Pair the domains. Evaluate the same policy versions in simulation and reality. Document the robot embodiment, hardware, sensors, control interface, task and scene so readers can tell what was held constant and what differed.
- Repeat across relevant conditions. Test randomized initial states and the distribution shifts that matter for the intended deployment. Describe the conditions and trial protocol, and report the number of trials. A single rollout is not evidence of typical performance.
- Use multiple alternatives for a predictive claim. Compare simulation and real-world scores across more than one policy or method-task condition. Report the correlation alongside the paired outcomes, and inspect exceptions rather than letting a summary statistic stand alone.
- Document discrepancies and failures. Describe relevant differences in visuals or control, any calibration or other mitigation, and the observed failure types, including safety-relevant outcomes.
- Scope the conclusion to what was tested. Name the benchmark, task family, embodiment and conditions. Do not infer predictive validity for a different robot or domain without evidence from that setting.
The Annual Review survey frames transfer as robust performance despite differences between simulated and real dynamics and observations; exact replication is not required. Treat this as the review’s framing, not a universal recipe or guarantee that a particular evaluation will transfer.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do published benchmarks show—and what do they not show?
Published results demonstrate why predictive validity must be measured in the setting where it will be used. They are evidence about their evaluated benchmarks, not universal thresholds for a good simulator.
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- SIMPLER: Li and co-authors’ 2025 PMLR paper, Evaluating Real-World Robot Manipulation Policies in Simulation, reports more than 1,500 paired simulation-and-real evaluations across two embodiments and eight manipulation task families, with strong correlation between simulated and real performance. The authors also report that simulated evaluations reflected policy sensitivity to distribution shifts in their evaluated manipulation settings. This supports SIMPLER as evidence for those setups, not as proof that simulation predicts performance equally well in navigation or locomotion.
- H2RBench: The project page for A Real-to-Sim Benchmark for Evaluating Human-to-Robot Transfer, marked CoRL 2026, reports Pearson r = 0.89, Spearman rho = 0.85 and MMRV = 0.06 across method-task configurations. These are benchmark-specific predictive-validity results for a human-to-robot transfer benchmark with four manipulation tasks reconstructed from real-world scenes; they should not be read as target values for other studies.
- Habitat evaluation: Kadian and co-authors’ 2020 IEEE Robotics and Automation Letters study, Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance?, reports SRCC of 0.18 for Habitat success, rising to 0.844 after simulator parameter tuning. The result illustrates that predictive validity can depend on simulator tuning; neither number is a generally expected range.
Choose a benchmark for the question, not its headline score
| Benchmark | Evaluated focus | Evidence described by its authors | Scope to keep in mind |
|---|---|---|---|
| SIMPLER | Common real-robot manipulation setups | Li et al. report paired simulation-and-real evaluations across two embodiments and eight task families | Manipulation results do not establish validity for navigation or locomotion |
| H2RBench | Human-to-robot transfer | Four manipulation tasks reconstructed from real-world scenes; its project page reports benchmark-specific correlation statistics across method-task configurations | Its Real2Sim protocol and task scope differ from SIMPLER’s evaluation purpose |
When selecting or comparing benchmarks, check whether their embodiment, observation and action interfaces, degree of real-world pairing, distribution-shift coverage and reproducibility match your question. H2RBench was designed around a shared protocol because earlier human-to-robot transfer evaluations differed on dimensions including embodiment, tasks, scenes, objects and real-world supervision conditions. Neither benchmark is established as suitable for every robotics study.
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How should results and limitations be reported?
A reproducible report makes it possible to distinguish a policy’s real-world capability from the simulator’s ability to predict it. Present these elements together:
- Robot embodiment and hardware, sensing and control interface.
- Task definition, success criterion, scene and objects, including any deviations between simulation and reality.
- Policy versions compared and the simulation and real-world score for each.
- Trial count, initial-state procedure, tested distribution shifts and the method used to summarize variation.
- Task metrics, reward definitions where relevant, failure categories and safety-relevant outcomes.
- Visual and control mismatches, calibration or mitigation, plus exceptions to the overall trend.
There is no universal minimum trial count, confidence-interval method or pass threshold established across manipulation, navigation and locomotion by the cited sources. State the choices used for the specific study instead of presenting a cutoff as a field-wide standard.
Mehta, Handa, Fox and Ramos noted in their 2021 PMLR paper A User’s Guide to Calibrating Robotic Simulators: “Despite significant progress on the development of sim-to-real algorithms, the analysis of different methods is still conducted in an ad-hoc manner without a consistent set of tests and metrics for comparison.” A consistent, clearly scoped protocol addresses that problem more directly than a claim based on visual fidelity alone: a convincing-looking simulator is not automatically predictive.
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