PDF-HR: Pose Distance Fields for Humanoid Robots

TL;DR

PDF-HR introduces a novel continuous pose prior for humanoid robots, enabling improved motion planning and control by measuring pose plausibility through a differentiable manifold, even with limited data.

Contribution

The paper presents PDF-HR, a lightweight, differentiable pose prior for humanoid robots that enhances motion tasks by providing a smooth plausibility measure.

Findings

PDF-HR improves motion tracking accuracy.

PDF-HR enhances motion mimicry and retargeting.

The prior integrates seamlessly into various pipelines.

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.