DYMO-Hair：面向機器人髮絲操控的通用化體積動力學建模

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

護髮作為一項日常必需活動，對於行動不便者而言卻難以觸及，同時由於頭髮精細的物理結構與複雜的動力學特性，這也對自主機器人系統構成了挑戰。本研究提出了DYMO-Hair，一個基於模型的機器人護髮系統。我們引入了一種新穎的動力學學習範式，該範式專為如頭髮這類體積量設計，依賴於動作條件下的潛在狀態編輯機制，並結合一個多樣化髮型的緊湊三維潛在空間，以提升泛化能力。此潛在空間通過一款新型頭髮物理模擬器進行大規模預訓練，從而實現對未見過髮型的廣泛適應。利用該動力學模型與模型預測路徑積分（MPPI）規劃器，DYMO-Hair能夠執行視覺目標導向的髮型設計。模擬實驗表明，DYMO-Hair的動力學模型在捕捉多樣化、未見過髮型的局部形變方面優於基準方法。在閉環髮型設計任務中，針對未見過髮型，DYMO-Hair進一步超越基準，其最終幾何誤差平均降低22%，成功率提高42%，相較於現有最先進系統。現實世界實驗展示了我們系統對假髮的零樣本遷移能力，在現有系統無法應對的挑戰性未見過髮型上實現了持續成功。這些成果共同為基於模型的機器人護髮奠定了基礎，推動了在無約束物理環境中更為通用、靈活且易於使用的機器人髮型設計技術的進步。更多詳情請訪問我們的項目頁面：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/.