Sim-to-real gap
The documented tendency for methods, particularly control policies trained through reinforcement learning or optimization, to perform differently, often worse, when transferred from simulation to physical hardware, due to unmodeled dynamics, sensor noise, and other real-world factors absent from the simulator. (see Topic 1.2) for first-principles reasoning about where a specific simulated result is likely to break on hardware.
Defined in 2 GAGE programs, which carry 11 distinct definitions of it. The wording above is taught in Engineering Judgment and Professional Formation.
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
The documented tendency for methods, particularly control policies trained through reinforcement learning or optimization, to perform differently, often worse, when transferred from simulation to physical hardware, due to unmodeled dynamics, sensor noise, and other real-world factors absent from the simulator. (see Topic 1.2) for first-principles reasoning about where a specific simulated result is likely to break on hardware.
The difference between how a robot behavior or learned policy performs in simulation and how it performs on physical hardware. Caused primarily by unmodeled friction and mechanical asymmetry, sensor noise characteristics that differ from simulation, and timing and latency differences between a simulated clock and a real system clock. Managing the sim-to-real gap is a central problem in Program 3's robot learning content.
The difference between robot behavior in simulation and behavior on real hardware. Arises from the approximations in any physical model: friction, inertia, sensor noise distributions, and communication timing are approximated rather than exactly replicated. Managed through domain randomization, calibrated simulation models, and targeted Phase 3 hardware tests.
The class of behavioral discrepancies that arise because a simulation approximates physical reality with simplified models of physics, timing, and sensor behavior. Not a hardware fault category. The diagnostic question is which physical property the algorithm depends on that differs between the simulated and real environments.
The specific instance of model mismatch that arises when a robot system validated in simulation is deployed in the real world and encounters physical dynamics, sensor behaviors, and environmental properties that differ from the simulation model.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.