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://dymohair.github.io/.

翻译：毛发护理是日常生活中的重要活动，但由于毛发精细的物理结构和复杂动力学特性，行动不便者难以独立完成，且对自主机器人系统构成挑战。本文提出DYMO-Hair——一种基于模型的机器人毛发护理系统。我们引入一种适用于体积量化对象（如毛发）的新型动力学学习范式，该范式依赖于动作条件化隐状态编辑机制，并结合包含多样发型的紧凑3D隐空间以提升泛化能力。该隐空间通过新型毛发物理仿真器进行大规模预训练，从而实现对未见发型的泛化。结合动力学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条件的发型设计。仿真实验表明，DYMO-Hair的动力学模型在捕捉多样未见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对未见发型的最终几何误差平均降低22%，成功率较最先进系统提升42%。真实世界实验展现出系统对假发的零样本迁移能力，在最先进系统失败的挑战性未见发型上持续取得成功。这些成果为基于模型的机器人毛发护理奠定了基础，推动在无约束物理环境中实现更泛化、灵活且易操作的机器人发型设计。更多详情请访问项目页面：https://dymohair.github.io/。