Sim-to-Real Gap: How to Diagnose Robot Policy Failures

Sim-to-real transfer in robotics often fails because simulation differs from hardware in dynamics, perception, or task distribution, with manipulation generally more vulnerable than quadruped locomotion because it depends on precise, nonredundant contact interactions. While domain randomization can improve transfer by broadening training conditions, indiscriminate randomization may waste model capacity and create overly conservative behavior, so the article advocates first localizing the source of failure. It recommends verifying simulation performance with meaningful confidence intervals, matching robot specifications and control interfaces across environments, and evaluating tasks from forgiving to precision-critical. Five common failure patterns—immediate action collapse, trajectory lag or offset, instability at contact, visual sensitivity, and workspace-dependent degradation—are linked to targeted tests for observation pipelines, actuator tracking, contact models, perception shifts, calibration, and training coverage. Offline comparisons of simulated and real observations, including raw sensor statistics, encoder embeddings, and action-prediction probes, can identify visual or data-processing mismatches without operating the robot. The central recommendation is to treat hardware failures as diagnostic evidence, fix bugs and calibration issues before applying randomization, and direct modeling, data collection, or randomization efforts only toward the subsystem shown to be responsible.

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