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.

翻译：姿态与运动先验在人形机器人学中扮演着关键角色。尽管此类先验已在人体运动恢复领域得到广泛研究，并发展出多种模型，但其在人形机器人中的应用仍十分有限，这主要归因于高质量人形机器人运动数据的稀缺。本工作提出了面向人形机器人的姿态距离场，这是一种轻量级先验模型，将机器人姿态分布表征为一个连续可微的流形。给定任意姿态，PDF-HR 可预测其与大规模重定向机器人姿态语料库之间的距离，从而生成一种适用于优化与控制任务的平滑姿态合理性度量。PDF-HR 可作为奖励塑形项、正则化器或独立合理性评分器，灵活集成到多种流程中。我们在多项人形机器人任务上评估了 PDF-HR，包括单轨迹运动跟踪、通用运动跟踪、基于风格的运动模仿以及通用运动重定向。实验表明，这种即插即用的先验模型能持续且显著增强现有强基线方法的性能。代码与模型将公开发布。