Learning Transfer-Aware Environment Curricula from Cross-Simulator Feedback

A harder navigation task need not offer more useful training for a physical robot. CrossSim-Curriculum selects geometry edits for cross-floor quadruped navigation by predicting their physical learning gain. Paired short updates in two simulators compare each edit with a control from the same checkpoint. A residual model calibrates these gains with sparse physical feedback; selection then accounts for uncertainty, cost, and edit-family coverage. We derive adaptive calibration intervals and local selection bounds, with seed, horizon, and route-shift allowances connecting probes to deployment. The evaluation protocol tests edit ranking and policy transfer across geometry splits, a held-out backend, and physical routes at matched budgets.