用连续可导的姿势场提升机器人动作合理性评估
PDF-HR: Pose Distance Fields for Humanoid Robots
- 将机器人姿态建模为连续可导流形，通过距离场衡量姿态合理性
- 在多种任务中显著提升基线模型表现，尤其在动作追踪与重定向上
- 轻量级设计适配各类控制与优化流程，可作奖励或正则项
姿态与运动先验在人形机器人领域至关重要。尽管在人体运动恢复（HMR）中已有广泛应用，但由于高质量人形机器人运动数据稀缺，其在机器人领域的应用仍有限。本文提出人形机器人姿态距离场（PDF-HR），一种轻量级先验模型，将机器人姿态分布表示为连续且可导的流形。对于任意姿态，PDF-HR 能预测其与大量重定向机器人姿态之间的距离，生成平滑的姿态合理性度量，适用于优化与控制。该模型可作为奖励塑造项、正则化项或独立合理性评分器，集成于多种流水线中。我们在单轨迹运动追踪、通用运动追踪、风格化动作模仿及通用运动重定向等任务上进行评估，结果表明该先验能持续且显著增强现有强基线模型。代码与模型将公开。
原文摘要 · Abstract (English)
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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