Sim-to-Real Deployment and Continual Correction › Domain Randomization: What It Buys You

Domain randomization trains across a declared distribution of environments or plants rather than a single nominal instance. The aim is to cover relevant deployment variation. Successful simulated performance across that family does not by itself guarantee physical transfer: omitted mechanisms, observations, tasks and interfaces can still differ. Whether broad training or deployment-time identification is preferable depends on what can be measured and changed.

Tobin et al. (2017) trained object localization from randomized synthetic images and used it for real robotic grasping. Sadeghi and Levine (2017) trained an indoor quadrotor visual collision-avoidance policy using randomized simulated imagery without real training images. Peng et al. (2018) varied dynamics, including mass, friction and control gains, for a robot-arm object-pushing task. Kumar et al. (2021) combined randomized locomotion training with an adaptation module in RMA. That module estimates task-relevant latent environmental context from proprioceptive state-and-action history, rather than directly identifying a physical parameter vector. They demonstrated physical quadruped deployment, including unseen terrains. These are task-specific empirical demonstrations, not universal guarantees. Successful transfer also does not prove that every property of the physical deployment was inside the declared simulated support.

Randomization can be applied to at least three distinct objects. These operations have different properties and should not be treated as interchangeable.

A policy trained across variation. Randomization supplies policy-training environments. A fixed policy can learn useful behavior across the sampled family without an explicit parameter-identification module. Average performance under that distribution is not a worst-case guarantee across every plant, and randomization alone does not make the policy robust by construction. A recurrent policy may also infer context implicitly from history.

A parameter-conditioned predictor. Randomization supplies predictor-training data with context: $\hat{s}_{t+1}=f_\theta(s_t,u_t,\phi)$, where $\phi=(k,c,b)$. Deployment must supply that context, through measurement, known configuration or identification. Wrong context can degrade predictions, but its effect depends on the predictor and the error. Context quality is one requirement alongside model fit and coverage, not their replacement.

A parameter-blind mixture predictor. Pooled regression receives state and action but no explicit plant parameters. Under squared loss an unrestricted optimum estimates the conditional mean next state given those inputs; a finite feature class approximates it. That conditional mean need not equal a transition at the mean plant parameters, because states can contain information about the plant. A pooled fit is not itself a robustness guarantee for the controller that uses it.

Below, the blind and conditioned regressions use identical transition pairs. The conditioned model additionally receives parameter context and six feature terms, and deployment context comes from an adaptation episode. This compares those specified fitting routes; it does not isolate context from representation capacity or adaptation information.

Wider randomization can require more training data and capacity, but it does not necessarily make a policy slower or less precise. Shared structure can support several plants without a fixed capacity cost per plant. Compare broad training, measured context and adaptation under matched budgets; the right choice depends on uncertainty, coverage and the cost of gathering deployment data.

There is also a coverage question: what happens outside the declared randomization family? Training-family results alone do not establish performance there. Membership is not a guarantee either: finite sampling can leave sparse regions within the support. Extrapolation may succeed or fail depending on structure; the shifted plant below probes one outside-family case, not a universal discontinuity at a support boundary.

Randomization by itself does not provide a runtime detector. A fixed trained system may nevertheless tolerate drift covered by its training and learned behavior. Monitoring checks deployment evidence as conditions evolve, including changes outside anticipated variation. The two mechanisms therefore address different requirements.