Generalizable Humanoid Manipulation with 3D Diffusion Policies

IEEE International Conference on Intelligent Robots and Systems (IROS 2025)

Yanjie Ze (1)    Zixuan Chen (2)    Wenhao Wang (3)    Tianyi Chen (3)    Xialin He (4)    Ying Yuan (5)    Xue Bin Peng (2)    Jiajun Wu (1)

(1) Stanford University    (2) Simon Fraser University    (3) University of Pennsylvania    (4) University of Illinois Urbana-Champaign    (5) Carnegie Mellon University

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Abstract

Humanoid robots capable of autonomous operation in diverse environments have long been a goal for roboticists. However, autonomous manipulation by humanoid robots has largely been restricted to one specific scene, primarily due to the difficulty of acquiring generalizable skills and the expensiveness of in-the-wild humanoid robot data. In this work, we build a real-world robotic system to address this challenging problem. Our system is mainly an integration of 1) a whole-upper-body robotic teleoperation system to acquire human-like robot data, 2) a 25-DoF humanoid robot platform with a height-adjustable cart and a 3D LiDAR sensor, and 3) an improved 3D Diffusion Policy learning algorithm for humanoid robots to learn from noisy human data. We run more than 2000 episodes of policy rollouts on the real robot for rigorous policy evaluation. Empowered by this system, we show that using only data collected in one single scene and with only onboard computing, a full-sized humanoid robot can autonomously perform skills in diverse real-world scenarios.

Paper: [PDF]       Webpage: [Link]       Preprint: [arXiv]       Code: [GitHub]

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Bibtex

@inproceedings{
  ze2025humanoid_manipulation,
  title = {Generalizable Humanoid Manipulation with 3D Diffusion Policies},
  author = {Yanjie Ze and Zixuan Chen and Wenhao Wang and Tianyi Chen and Xialin He and Ying Yuan and Xue Bin Peng and Jiajun Wu},
  booktitle = {2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages = {2873-2880},
  year = {2025}
}