PHIL: Proactive human-in-the-loop intervention via preference learning

The paper proposes the PHIL framework, which lets operators preview a policy's predicted future actions through an XR interface, take over to correct before undesirable actions occur, and pair rejected predictions with corrective actions for preference post-training. On four real manipulation tasks, with five correction episodes per condition, mean success rates rose from 44.0% to 70.3% for the diffusion policy and from 57.8% to 76.9% for X-VLA, compared with the HG-DAgger baseline; policies trained with proactive corrections showed fewer unnecessary recovery behaviors in successful episodes.