title: UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
summary: A generalist robot should perform effectively across various environments.
  However, most existing approaches heavily rely on scaling action-annotated data
  to enhance their capabilities. Consequently, they are often limited to single
  physical specification and struggle to learn transferable knowledge across
  different embodiments and environments. To confront these limitations, we
  propose UniVLA, a new framework for learning cross-embodiment
  vision-language-action (VLA) policies. Our key innovation is to derive
  task-centric action representations from videos with a latent action model.
  This enables us to exploit extensive data across a wide spectrum of embodiments
  and perspectives. To mitigate the effect of task-irrelevant dynamics, we
  incorporate language instructions and establish a latent action model within
  the DINO feature space. Learned from internet-scale videos, the generalist
  policy can be deployed to various robots through efficient latent action
  decoding. We obtain state-of-the-art results across multiple manipulation and
  navigation benchmarks, as well as real-robot deployments. UniVLA achieves
  superior performance over OpenVLA with less than 1/20 of pretraining compute
  and 1/10 of downstream data. Continuous performance improvements are observed
  as heterogeneous data, even including human videos, are incorporated into the
  training pipeline. The results underscore UniVLA's potential to facilitate
  scalable and efficient robot policy learning.
paper_authors._id: 68218b847202d193249511b6
paper_authors.name: Qingwen Bu
paper_authors.status: admin_assigned
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paper_authors.fullname: Qingwen Bu
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paper_authors._id: 68218b847202d193249511b7
paper_authors.name: Yanting Yang
paper_authors._id: 68218b847202d193249511b8
paper_authors.name: Jisong Cai
paper_authors.status: admin_assigned
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paper_authors.fullname: Jisong Cai
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paper_authors.user: SereneC
paper_authors._id: 68218b847202d193249511b9
paper_authors.name: Shenyuan Gao
paper_authors.status: admin_assigned
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paper_authors.fullname: Shenyuan Gao
paper_authors.type: user
paper_authors.user: Little-Podi
paper_authors._id: 68218b847202d193249511ba
paper_authors.name: Guanghui Ren
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paper_authors.fullname: Guanghui Ren
paper_authors.type: user
paper_authors.user: sundrops
paper_authors._id: 68218b847202d193249511bb
paper_authors.name: Maoqing Yao
paper_authors.status: admin_assigned
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paper_authors._id: 67739bfa64e8b7438ae68eb4
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paper_authors.fullname: Maoqing Yao
paper_authors.type: user
paper_authors.user: AutobotZero
paper_authors._id: 68218b847202d193249511bc
paper_authors.name: Ping Luo
paper_authors.status: admin_assigned
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paper_authors._id: 67cb7d55560c3dcbb1adeaa3
paper_authors.avatarUrl: /avatars/0b616d3655b0b54a621c2608b2f14379.svg
paper_authors.fullname: Ping Luo
paper_authors.type: user
paper_authors.user: appleluo
paper_authors._id: 68218b847202d193249511bd
paper_authors.name: Hongyang Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-12T13:16:21.369Z
paper_authors._id: 6499b0184936457997180c90
paper_authors.avatarUrl: /avatars/b8be7bfabf746639e30330f5f623f560.svg
paper_authors.fullname: Hongyang Li
paper_authors.type: user
paper_authors.user: compileme
paper_title: UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
paper_summary: A generalist robot should perform effectively across various environments.
  However, most existing approaches heavily rely on scaling action-annotated data
  to enhance their capabilities. Consequently, they are often limited to single
  physical specification and struggle to learn transferable knowledge across
  different embodiments and environments. To confront these limitations, we
  propose UniVLA, a new framework for learning cross-embodiment
  vision-language-action (VLA) policies. Our key innovation is to derive
  task-centric action representations from videos with a latent action model.
  This enables us to exploit extensive data across a wide spectrum of embodiments
  and perspectives. To mitigate the effect of task-irrelevant dynamics, we
  incorporate language instructions and establish a latent action model within
  the DINO feature space. Learned from internet-scale videos, the generalist
  policy can be deployed to various robots through efficient latent action
  decoding. We obtain state-of-the-art results across multiple manipulation and
  navigation benchmarks, as well as real-robot deployments. UniVLA achieves
  superior performance over OpenVLA with less than 1/20 of pretraining compute
  and 1/10 of downstream data. Continuous performance improvements are observed
  as heterogeneous data, even including human videos, are incorporated into the
  training pipeline. The results underscore UniVLA's potential to facilitate
  scalable and efficient robot policy learning.
paper_ai_summary: UniVLA is a framework for learning cross-embodiment VLA policies using latent action models derived from internet-scale videos, achieving superior performance with reduced pretraining compute and downstream data.
paper_ai_keywords: latent action model VLA policies DINO feature space