DYMO-Hair: Generalizable Volumetric Dynamics Modeling for Robot Hair
  Manipulation
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/.
68e702757ae125f9582e684b
Chengyang Zhao
68e702757ae125f9582e684c
Uksang Yoo
68e702757ae125f9582e684d
Arkadeep Narayan Chaudhury
68e702757ae125f9582e684e
Giljoo Nam
68e702757ae125f9582e684f
Jonathan Francis
68e702757ae125f9582e6850
Jeffrey Ichnowski
68e702757ae125f9582e6851
Jean Oh
DYMO-Hair: Generalizable Volumetric Dynamics Modeling for Robot Hair
  Manipulation
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/.
DYMO-Hair, a model-based robot hair care system, uses a novel dynamics learning paradigm and a 3D latent space to perform visual goal-conditioned hair styling with high accuracy and generalizability.
dynamics learning paradigm action-conditioned latent state editing 3D latent space hair physics simulator Model Predictive Path Integral (MPPI) planner visual goal-conditioned hair styling local deformation closed-loop hair styling tasks zero-shot transferability