PDF-HR: Pose Distance Fields for Humanoid Robots

• URL: http://arxiv.org/abs/2602.04851v1
• Date: Wed, 04 Feb 2026 18:38:51 GMT
• Title: PDF-HR: Pose Distance Fields for Humanoid Robots
• Abstract summary: We introduce Pose Distance Fields for Humanoid Robots (PDF-HR)<n>PDF-HR predicts its distance to a large corpus of retargeted robot poses, yielding a smooth measure of pose plausibility.<n>We evaluate PDF-HR on various humanoid tasks, including single-trajectory motion tracking, general motion tracking, style-based motion mimicry, and general motion tracking.
• Score: 43.82560516577811
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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), 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.

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