title: UniSkill: Imitating Human Videos via Cross-Embodiment Skill
    Representations
summary: Mimicry is a fundamental learning mechanism in humans, enabling individuals
  to learn new tasks by observing and imitating experts. However, applying this
  ability to robots presents significant challenges due to the inherent
  differences between human and robot embodiments in both their visual appearance
  and physical capabilities. While previous methods bridge this gap using
  cross-embodiment datasets with shared scenes and tasks, collecting such aligned
  data between humans and robots at scale is not trivial. In this paper, we
  propose UniSkill, a novel framework that learns embodiment-agnostic skill
  representations from large-scale cross-embodiment video data without any
  labels, enabling skills extracted from human video prompts to effectively
  transfer to robot policies trained only on robot data. Our experiments in both
  simulation and real-world environments show that our cross-embodiment skills
  successfully guide robots in selecting appropriate actions, even with unseen
  video prompts. The project website can be found at:
  https://kimhanjung.github.io/UniSkill.
paper_authors._id: 6823f20eb9e5c4a5c866a634
paper_authors.name: Hanjung Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-15T10:32:41.278Z
paper_authors._id: 66f21f9e14fcea0aa11361f5
paper_authors.avatarUrl: /avatars/fb8261fb9da98f47d9102d68762ac821.svg
paper_authors.fullname: Hanjung Kim
paper_authors.type: user
paper_authors.user: HanjungKim
paper_authors._id: 6823f20eb9e5c4a5c866a635
paper_authors.name: Jaehyun Kang
paper_authors._id: 6823f20eb9e5c4a5c866a636
paper_authors.name: Hyolim Kang
paper_authors._id: 6823f20eb9e5c4a5c866a637
paper_authors.name: Meedeum Cho
paper_authors._id: 6823f20eb9e5c4a5c866a638
paper_authors.name: Seon Joo Kim
paper_authors._id: 6823f20eb9e5c4a5c866a639
paper_authors.name: Youngwoon Lee
paper_title: UniSkill: Imitating Human Videos via Cross-Embodiment Skill
    Representations
paper_summary: Mimicry is a fundamental learning mechanism in humans, enabling individuals
  to learn new tasks by observing and imitating experts. However, applying this
  ability to robots presents significant challenges due to the inherent
  differences between human and robot embodiments in both their visual appearance
  and physical capabilities. While previous methods bridge this gap using
  cross-embodiment datasets with shared scenes and tasks, collecting such aligned
  data between humans and robots at scale is not trivial. In this paper, we
  propose UniSkill, a novel framework that learns embodiment-agnostic skill
  representations from large-scale cross-embodiment video data without any
  labels, enabling skills extracted from human video prompts to effectively
  transfer to robot policies trained only on robot data. Our experiments in both
  simulation and real-world environments show that our cross-embodiment skills
  successfully guide robots in selecting appropriate actions, even with unseen
  video prompts. The project website can be found at:
  https://kimhanjung.github.io/UniSkill.
paper_ai_summary: UniSkill learns embodiment-agnostic skill representations from unlabeled cross-embodiment video data, enabling human skill transfer to robots in both simulation and real-world settings.
paper_ai_keywords: cross-embodiment datasets skill representations cross-embodiment skills robot policies video prompts