CG-THWM: Curriculum-Guided Temporal Haptic World Modeling for Peg-In-Hole Tasks

Abstract

Fine-tolerance peg-in-hole manipulation demands high precision under contact-rich, nonsmooth dynamics, where irregular geometries, inclinations, and tight-clearance interference often cause model-free reinforcement learning (RL) to fail. We propose the Curriculum-Guided Temporal Haptic World Model (CG-THWM), which couples a world model with temporal haptic information and trains it via a staged curriculum. The world model supports efficient long-horizon planning with value estimation, while temporal haptic signals expose critical contact events; the curriculum stabilizes training and improves generalization. To enable rigorous evaluation, we construct a dataset for complex insertions that covers irregular, inclined, and interference-rich settings. In simulation, CG-THWM attains a 100% success rate on standard baselines and a 70% mean success rate in scenarios where conventional RL fails. These results highlight CG-THWM's potential for industrial and service applications.