DYMO-Hair: Generalizable Volumetric Dynamics Modeling for Robot Hair
  Manipulation
Authors: name: Chengyang Zhao, name: Uksang Yoo, name: Arkadeep Narayan Chaudhury, name: Giljoo Nam, name: Jonathan Francis, name: Jeffrey Ichnowski, name: Jean Oh

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/.