AutoDAgger lets compact robot policies learn from a teacher with far less data

Researchers introduced AutoDAgger, a method that recovers teacher trajectories during a student policy's own rollouts instead of in a separate data-collection stage. It collects recovery data more than twice as fast as the human-monitored HG-DAgger and removes the need for a person to supervise the process. Using an autonomous reward model to trigger teacher takeovers, a student policy rose from a baseline score of 27.8 to 63.9 after 30 episodes and 77.8 after 90 episodes. The TILDE approach reportedly outperformed all evaluated baselines on three manipulation tasks, though the study tested only those tasks and assumed a reliable reward signal. Its robustness under sparse teacher interventions or noisy rewards remains unresolved.