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Synthetic data gives ISR perception teams labeled EO/IR and SAR imagery of rare targets they cannot collect or share. Train on it with some real data, then test on real.
VLA policies fail by grabbing the wrong object, missing grasps, closing on air, stalling and not recovering, and they get worse when cameras, layouts or wording change.
Synthetic data gives USV lookout models the rare, dangerous cases real footage lacks, like swimmers, debris, night and rough seas. Train on it, validate on real video.
Isaac Lab-Arena, RoboLab, LeRobot and SimplerEnv evaluate robot policies in simulation, RoboArena and AutoEval on real robots. The right one depends on your benchmark.
We ran the public pi0.5 LIBERO checkpoint for 1,999 episodes across all four suites and averaged 96.7%, against 96.85% reported by openpi. Per-suite results and rerun spread.
About 100 trials gives a 95% interval of roughly ±8 to ±10 points on one success rate. Telling two policies 10 points apart takes 199 to 388 trials each.
The best synthetic data platform depends on your domain and sensors. We compare NVIDIA, Duality, Parallel Domain, Rendered.ai, Anyverse and others as of October 2026.
Run each LIBERO suite from its 50 fixed initial states, 50 episodes per task, with step limits of 220 to 520, then match the training image rotation and action chunking.
For ranking policies, often yes. SIMPLER reports a 0.924 Pearson correlation with real results and PolaRiS 0.90, but sim success rates rarely match real ones.
Largely, yes. Top VLAs report 96 to 98% averages, but LIBERO-PRO and LIBERO-Plus perturbations drop several to near 0% when objects move, tasks change or cameras shift.
The best VLA simulation benchmarks in 2026 are LIBERO-Plus or LIBERO-PRO, RoboCasa365, RoboTwin 2.0, RoboLab and SimplerEnv. Plain LIBERO is saturated at 97.1%.
Labeled thermal IR training data comes from three routes: hand-labeling real frames, translating RGB images to thermal, or rendering scenes with physics-based simulation.
Measure the gap on held-out real data, model your sensor, randomize appearance, match the real content distribution, then fine-tune on a small real set.
Yes, mostly as a complement. Pretraining on synthetic images and fine-tuning on a small real set beats real-only training in most published results.
The main SimplerEnv alternatives are PolaRiS for DROID policies, RoboLab in Isaac Lab-Arena, Gaussian-splat soft-body twins, RobotArena ∞, and real-world RoboArena.
Synthetic data improves drone detectors most when mixed with real images. Published hybrids beat real-only training, while synthetic-only models trail on hard test sets.
For policy evaluation, the benchmark usually picks the simulator. LIBERO and RoboCasa run on MuJoCo, SimplerEnv on ManiSkill, Isaac Lab-Arena tasks on Isaac Lab.
Eval time goes to policy inference, sim stepping, rendering and resets. Vectorize environments, shard across GPUs, batch inference and serve the policy separately.
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