Jiajun Liu

The work presents RoboCoach, a world-model-guided active coaching framework whose Route-Imagine-Diagnose-Improve (RIDI) loop executes reusable skill experts in closed loop inside CoachWorld, a shared action-conditioned world model, uses a progress judge to record the first subtask that fails to complete, and thereby selects which subtask demonstrations to acquire and which expert adapters to update; across two simulation suites and two real-robot platforms, imagined and deployed success correlate with Spearman rho = 0.840 over 22 task-policy pairs, and with only 150 additional subtask demonstrations per platform success rises from 13.3% to 75.0% on Franka and from 40.0% to 83.8% on AgileX, while the coached experts reach 35.

The work presents RoboCoach, a world-model-guided active coaching framework whose Route-Imagine-Diagnose-Improve (RIDI) loop executes reusable skill experts in closed loop inside CoachWorld, a shared action-conditioned world model, uses a progress judge to record the first subtask that fails to complete, and thereby selects which subtask demonstrations to acquire and which expert adapters to update; across two simulation suites and two real-robot platforms, imagined and deployed success correlate with Spearman rho = 0.840 over 22 task-policy pairs, and with only 150 additional subtask demonstrations per platform success rises from 13.3% to 75.0% on Franka and from 40.0% to 83.8% on AgileX, while the coached experts reach 35.