Pose Distance Fields for Humanoid Robots
Pose Distance Fields for Humanoid Robots
This paper presents Pose Distance Fields for Humanoid Robots (PDF-HR), a lightweight prior that encodes robot pose distributions as a continuous, differentiable manifold. By predicting the distance from an arbitrary pose to a large corpus of retargeted robot poses, PDF-HR provides a smooth measure of pose plausibility suitable for optimization and control. The method can be integrated as a reward shaping term, regularizer, or standalone scorer across diverse pipelines. Experiments on humanoid tasks including single-trajectory motion tracking, general motion tracking, style-based motion mimicry, and general motion retargeting show that PDF-HR consistently strengthens strong baselines. Code and models are publicly available.
Source: arXiv Robotics — research abstracts · Read original article ↗
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arXiv:2602.04851v2 Announce Type: replace Abstract: 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),
Open source S2
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 diver
Open source S3
se 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 are publicly available at https://gaoyukang33.github.io/PDF-HR.
Open source S4
Pose Distance Fields for Humanoid Robots
Open source S1
Source：arXiv Robotics — research abstracts · arxiv.org