Pose and motion priors play a crucial role in humanoid robotics. Although such priors have been widely studied in human motion recovery (HMR) domain with a range of models, their adoption for humanoid robots remains limited, largely due to the scarcity of high-quality humanoid motion data. In this work, we introduce Pose Distance Fields for Humanoid Robots (PDF-HR), a lightweight prior that represents the robot pose distribution as a continuous and differentiable manifold. Given an arbitrary pose, PDF-HR predicts its distance to a large corpus of retargeted robot poses, yielding a smooth measure of pose plausibility that is well suited for optimization and control. PDF-HR can be integrated as a reward shaping term, a regularizer, or a standalone plausibility scorer across diverse pipelines. We evaluate PDF-HR on various humanoid tasks, including single-trajectory motion tracking, general motion tracking, style-based motion mimicry, and general motion retargeting. Experiments show that this plug-and-play prior consistently and substantially strengthens strong baselines. Code and models will be released.
Cite
@article{arxiv.2602.04851,
title = {PDF-HR: Pose Distance Fields for Humanoid Robots},
author = {Yi Gu and Yukang Gao and Yangchen Zhou and Xingyu Chen and Yixiao Feng and Mingle Zhao and Yunyang Mo and Zhaorui Wang and Lixin Xu and Renjing Xu},
journal= {arXiv preprint arXiv:2602.04851},
year = {2026}
}