Executing reliable Humanoid-Object Interaction (HOI) tasks for humanoid robots is hindered by the lack of generalized control interfaces and robust closed-loop perception mechanisms. In this work, we introduce Perceptive Root-guided Humanoid-Object Interaction, Pro-HOI, a generalizable framework for robust humanoid loco-manipulation. First, we collect box-carrying motions that are suitable for real-world deployment and optimize penetration artifacts through a Signed Distance Field loss. Second, we propose a novel training framework that conditions the policy on a desired root-trajectory while utilizing reference motion exclusively as a reward. This design not only eliminates the need for intricate reward tuning but also establishes root trajectory as a universal interface for high-level planners, enabling simultaneous navigation and loco-manipulation. Furthermore, to ensure operational reliability, we incorporate a persistent object estimation module. By fusing real-time detection with Digital Twin, this module allows the robot to autonomously detect slippage and trigger re-grasping maneuvers. Empirical validation on a Unitree G1 robot demonstrates that Pro-HOI significantly outperforms baselines in generalization and robustness, achieving reliable long-horizon execution in complex real-world scenarios.
@article{arxiv.2603.01126,
title = {Pro-HOI: Perceptive Root-guided Humanoid-Object Interaction},
author = {Yuhang Lin and Jiyuan Shi and Dewei Wang and Jipeng Kong and Yong Liu and Chenjia Bai and Xuelong Li},
journal= {arXiv preprint arXiv:2603.01126},
year = {2026}
}