Enabling robust whole-body humanoid-object interaction (HOI) remains challenging due to motion data scarcity and the contact-rich nature. We present HDMI (HumanoiD iMitation for Interaction), a simple and general framework that learns whole-body humanoid-object interaction skills directly from monocular RGB videos. Our pipeline (i) extracts and retargets human and object trajectories from unconstrained videos to build structured motion datasets, (ii) trains a reinforcement learning (RL) policy to co-track robot and object states with three key designs: a unified object representation, a residual action space, and a general interaction reward, and (iii) zero-shot deploys the RL policies on real humanoid robots. Extensive sim-to-real experiments on a Unitree G1 humanoid demonstrate the robustness and generality of our approach: HDMI achieves 67 consecutive door traversals and successfully performs 6 distinct loco-manipulation tasks in the real world and 14 tasks in simulation. Our results establish HDMI as a simple and general framework for acquiring interactive humanoid skills from human videos.
@article{arxiv.2509.16757,
title = {HDMI: Learning Interactive Humanoid Whole-Body Control from Human Videos},
author = {Haoyang Weng and Yitang Li and Nikhil Sobanbabu and Zihan Wang and Zhengyi Luo and Tairan He and Deva Ramanan and Guanya Shi},
journal= {arXiv preprint arXiv:2509.16757},
year = {2025}
}