title: LoHoVLA: A Unified Vision-Language-Action Model for Long-Horizon
    Embodied Tasks
summary: Real-world embodied agents face long-horizon tasks, characterized by
  high-level goals demanding multi-step solutions beyond single actions.
  Successfully navigating these requires both high-level task planning (i.e.,
  decomposing goals into sub-tasks) and low-level motion control (i.e.,
  generating precise robot actions). While existing vision language action (VLA)
  models and hierarchical architectures offer potential in embodied tasks, the
  former often falter in planning, and the latter can suffer from coordination
  issues, both hampering performance. We introduce a new unified VLA framework
  for long-horizon tasks, dubbed LoHoVLA, to overcome these limitations. LoHoVLA
  leverages a large pretrained vision language model (VLM) as the backbone to
  jointly generate language and action tokens for sub-task generation and robot
  action prediction, respectively. This shared representation promotes better
  generalization across tasks. Additionally, LoHoVLA embraces a hierarchical
  closed-loop control mechanism to mitigate errors originating from both
  high-level planning and low-level control. To train LoHoVLA, we introduce
  LoHoSet, a dataset built on the Ravens simulator, containing 20 long-horizon
  tasks, each with 1,000 expert demonstrations composed of visual observations,
  linguistic goals, sub-tasks, and robot actions. Experimental results show that
  LoHoVLA significantly surpasses both hierarchical and standard VLA approaches
  on long-horizon embodied tasks in the Ravens simulator. These findings
  underscore the promise of unified architectures for advancing generalizable
  embodied intelligence.
paper_authors._id: 683e86e31bca54bb6d169fc4
paper_authors.name: Yi Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-03T08:40:09.713Z
paper_authors._id: 6707eaaf5f50b17754ff9cbc
paper_authors.avatarUrl: /avatars/f08ca6228b124a8955787d0662a52bbd.svg
paper_authors.fullname: Yang
paper_authors.type: user
paper_authors.user: Yysrc
paper_authors._id: 683e86e31bca54bb6d169fc5
paper_authors.name: Jiaxuan Sun
paper_authors._id: 683e86e31bca54bb6d169fc6
paper_authors.name: Siqi Kou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-03T12:35:37.198Z
paper_authors._id: 654e330f350abceb30a1390b
paper_authors.avatarUrl: /avatars/e54a8be788fa1bdc7acefecc208215bb.svg
paper_authors.fullname: KouSiqi
paper_authors.type: user
paper_authors.user: karrykkk
paper_authors._id: 683e86e31bca54bb6d169fc7
paper_authors.name: Yihan Wang
paper_authors._id: 683e86e31bca54bb6d169fc8
paper_authors.name: Zhijie Deng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-03T12:35:13.092Z
paper_authors._id: 673d5f411b0fe168ad4896b2
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/fYgQx6XNi_P5GxlUKbH5G.png
paper_authors.fullname: Zhijie Deng
paper_authors.type: user
paper_authors.user: thudzj
paper_title: LoHoVLA: A Unified Vision-Language-Action Model for Long-Horizon
    Embodied Tasks
paper_summary: Real-world embodied agents face long-horizon tasks, characterized by
  high-level goals demanding multi-step solutions beyond single actions.
  Successfully navigating these requires both high-level task planning (i.e.,
  decomposing goals into sub-tasks) and low-level motion control (i.e.,
  generating precise robot actions). While existing vision language action (VLA)
  models and hierarchical architectures offer potential in embodied tasks, the
  former often falter in planning, and the latter can suffer from coordination
  issues, both hampering performance. We introduce a new unified VLA framework
  for long-horizon tasks, dubbed LoHoVLA, to overcome these limitations. LoHoVLA
  leverages a large pretrained vision language model (VLM) as the backbone to
  jointly generate language and action tokens for sub-task generation and robot
  action prediction, respectively. This shared representation promotes better
  generalization across tasks. Additionally, LoHoVLA embraces a hierarchical
  closed-loop control mechanism to mitigate errors originating from both
  high-level planning and low-level control. To train LoHoVLA, we introduce
  LoHoSet, a dataset built on the Ravens simulator, containing 20 long-horizon
  tasks, each with 1,000 expert demonstrations composed of visual observations,
  linguistic goals, sub-tasks, and robot actions. Experimental results show that
  LoHoVLA significantly surpasses both hierarchical and standard VLA approaches
  on long-horizon embodied tasks in the Ravens simulator. These findings
  underscore the promise of unified architectures for advancing generalizable
  embodied intelligence.
paper_ai_summary: A unified vision language action framework, LoHoVLA, combines a large pretrained vision language model with hierarchical closed-loop control to improve performance on long-horizon embodied tasks.
paper_ai_keywords: vision language action models hierarchical architectures high-level task planning low-level motion control LoHoVLA large pretrained vision language model shared representation hierarchical closed-loop control LoHoSet Ravens simulator long-horizon tasks sub-task generation robot action prediction embodied intelligence