DYMO-Hair：面向机器人毛发操作的通用体积动力学建模

DYMO-Hair: Generalizable Volumetric Dynamics Modeling for Robot Hair Manipulation

October 7, 2025

作者: Chengyang Zhao, Uksang Yoo, Arkadeep Narayan Chaudhury, Giljoo Nam, Jonathan Francis, Jeffrey Ichnowski, Jean Oh

cs.AI

摘要

头发护理是日常生活中不可或缺的活动，然而对于行动不便者而言难以触及，对自主机器人系统来说也颇具挑战，这源于头发精细的物理结构和复杂的动态特性。本研究提出了DYMO-Hair，一个基于模型的机器人头发护理系统。我们引入了一种新颖的动态学习范式，该范式适用于如头发这样的体积性物质，依赖于动作条件下的潜在状态编辑机制，并结合了一个紧凑的3D潜在空间，该空间涵盖了多样化的发型，以提升泛化能力。此潜在空间通过一个创新的头发物理模拟器进行大规模预训练，从而实现对未见发型的泛化。利用该动态模型与模型预测路径积分（MPPI）规划器，DYMO-Hair能够执行基于视觉目标的发型设计。仿真实验表明，DYMO-Hair的动态模型在捕捉多样化、未见发型的局部变形方面优于基线方法。在闭环发型设计任务中，DYMO-Hair对未见发型的处理也超越了基线，平均几何误差降低了22%，成功率提高了42%，相较于当前最先进的系统。真实世界实验展示了我们的系统对假发的零样本迁移能力，在极具挑战性的未见发型上实现了持续的成功，而现有最先进系统则无法做到。这些成果共同为基于模型的机器人头发护理奠定了基础，推动着在无约束物理环境中实现更通用、灵活且易于获取的机器人发型设计。更多详情请访问我们的项目页面：https://chengyzhao.github.io/DYMOHair-web/。

English

Hair care is an essential daily activity, yet it remains inaccessible to individuals with limited mobility and challenging for autonomous robot systems due to the fine-grained physical structure and complex dynamics of hair. In this work, we present DYMO-Hair, a model-based robot hair care system. We introduce a novel dynamics learning paradigm that is suited for volumetric quantities such as hair, relying on an action-conditioned latent state editing mechanism, coupled with a compact 3D latent space of diverse hairstyles to improve generalizability. This latent space is pre-trained at scale using a novel hair physics simulator, enabling generalization across previously unseen hairstyles. Using the dynamics model with a Model Predictive Path Integral (MPPI) planner, DYMO-Hair is able to perform visual goal-conditioned hair styling. Experiments in simulation demonstrate that DYMO-Hair's dynamics model outperforms baselines on capturing local deformation for diverse, unseen hairstyles. DYMO-Hair further outperforms baselines in closed-loop hair styling tasks on unseen hairstyles, with an average of 22% lower final geometric error and 42% higher success rate than the state-of-the-art system. Real-world experiments exhibit zero-shot transferability of our system to wigs, achieving consistent success on challenging unseen hairstyles where the state-of-the-art system fails. Together, these results introduce a foundation for model-based robot hair care, advancing toward more generalizable, flexible, and accessible robot hair styling in unconstrained physical environments. More details are available on our project page: https://chengyzhao.github.io/DYMOHair-web/.