Sim-to-Real Deployment and Continual Correction › Sim-to-Real Deployment and Continual Correction

A better fitted dynamics model need not give better tracking with a fixed planner. The synthetic experiment in this chapter demonstrates that distinction while separating parameter mismatch, delayed observations and actuator limits. We develop a reproducible deployment workflow: collect informative adaptation data, monitor prediction errors, and evaluate proposed corrections before promotion. These executable results are not hardware evidence or a safety certificate.

Simulation-to-reality transfer can expose parametric mismatch, structural mismatch, observation differences, and task or interface errors. Parametric mismatch means the chosen model family can represent the relevant plant but its numerical values are wrong, such as damping after a payload change. Identifying those values requires observations that distinguish the parameters; sufficient excitation can provide that information. Good conditioning reduces sensitivity to measurement noise and numerical error, rather than being a logical requirement for exact noiseless recovery. Structural mismatch means the relevant true map is outside the fixed hypothesis class. Retuning parameters cannot make a member of that class equal an excluded map over the whole domain. This does not imply a positive approximation-error floor or a permanent observed residual: a nonclosed class can approach the map arbitrarily closely, and finite observations can miss the discrepant region.

The remedies address different parts of this problem. Domain randomization supplies training variation within a specified family; it does not automatically add an omitted mechanism. Residual models fit discrepancies relative to a nominal predictor, with generalization depending on their structure and adaptation data. Monitoring checks for departures from a reference error pattern; detection depends on distinguishable observations and a suitable decision rule, and does not by itself identify the cause. Continual correction adds a controlled update process, including rejection, versioning and rollback.

The rest of Part XII has already given you the tools to talk about what goes wrong. In Failure Modes and Model Exploitation we saw how optimization against an imperfect model can exploit and amplify accessible errors. In Robustness, Calibration, and Safe Control we developed uncertainty calibration and runtime assurance. We will not re-derive those here. Instead we will take a small, fully executable system, deliberately break it in controlled ways, and watch which remedies help, which do not, and why. Controlled comparisons help attribute effects to specified changes. Our delay and actuator-authority conditions add one factor at a time to an already parameter-shifted deployment baseline; they are not single-discrepancy comparisons against the nominal plant. Hardware can combine multiple discrepancies, motivating matched ablations rather than attribution from an aggregate score.