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'The simulation score is high, so it will work on hardware': where that breaks

The claim
The implication most common in paper abstracts and demo reels: a policy scoring X% on a simulation benchmark will perform close to X% when deployed on real hardware.
Reality
What real-hardware reproduction usually shows: the sim score is X%, the hardware score is clearly lower, and the more complex the task the further it falls; contact-rich tasks (insertion, deformables) barely transfer at all without real data or domain randomisation. The drop is not one thing called 'inaccurate simulation' — it is contact modelling, visual differences, mechanism backlash and sensor noise stacking, and any single one of them can take a task from 'works' to 'does not'.
Where the gap is
The gap is that 'the simulation validated the policy's logic' and 'the simulation guaranteed the policy's physical feasibility' are two different claims. A sim score proves the first — the policy learned the task's structure. It cannot prove the second — real contacts and noise are less forgiving than the model. The demo cases that 'perform close to sim' have almost always compensated real parameters in the same pipeline; that step just never makes the abstract.
When it closes
The gap narrows substantially when three things are in place: ① system identification with real-hardware data (friction, inertia, actuator response); ② domain randomisation (training against jittered parameters in sim); ③ safety nets and retry logic on hardware (reporting 'success within retries' rather than single-attempt rates). The number will always sit below the sim score — but that margin moves from 'unusable' to 'acceptable' through these three steps, not through a bigger model.
This site's call

Treat simulation as a filter, not a promise. A policy that scores well in sim is worth trying on hardware; do not copy the number into your expectations. The practical rule for builders: get the motion 'visibly stable' in sim before hardware — every real attempt costs a hundred times a sim rollout, so let simulation do what it is for: eliminating bad candidates cheaply.

Four sources of the drop

SourceIn simulationOn hardware
Contact modelIdealised friction and restitutionWear, grime and deformation keep changing friction
VisionClean renders, controllable lightReflections, shadows, white-balance drift
BacklashRigid joints, zero playPrinted parts and gearboxes all have play
Sensor noiseOften omitted or idealisedIMU vibration, encoder quantisation, dropped frames

Sources

  • Sim-to-real transfer literature and reproduction discussions (domain randomisation, system identification)

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Last checked 2026-09-28