title: Being-0: A Humanoid Robotic Agent with Vision-Language Models and
    Modular Skills
summary: Building autonomous robotic agents capable of achieving human-level
  performance in real-world embodied tasks is an ultimate goal in humanoid robot
  research. Recent advances have made significant progress in high-level
  cognition with Foundation Models (FMs) and low-level skill development for
  humanoid robots. However, directly combining these components often results in
  poor robustness and efficiency due to compounding errors in long-horizon tasks
  and the varied latency of different modules. We introduce Being-0, a
  hierarchical agent framework that integrates an FM with a modular skill
  library. The FM handles high-level cognitive tasks such as instruction
  understanding, task planning, and reasoning, while the skill library provides
  stable locomotion and dexterous manipulation for low-level control. To bridge
  the gap between these levels, we propose a novel Connector module, powered by a
  lightweight vision-language model (VLM). The Connector enhances the FM's
  embodied capabilities by translating language-based plans into actionable skill
  commands and dynamically coordinating locomotion and manipulation to improve
  task success. With all components, except the FM, deployable on low-cost
  onboard computation devices, Being-0 achieves efficient, real-time performance
  on a full-sized humanoid robot equipped with dexterous hands and active vision.
  Extensive experiments in large indoor environments demonstrate Being-0's
  effectiveness in solving complex, long-horizon tasks that require challenging
  navigation and manipulation subtasks. For further details and videos, visit
  https://beingbeyond.github.io/being-0.
paper_authors._id: 67d8eadc045f869fea1ce3f2
paper_authors.name: Haoqi Yuan
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paper_authors.fullname: Haoqi Yuan
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paper_authors.name: Yu Bai
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paper_authors.fullname: Yu Bai
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paper_authors.name: Yuhui Fu
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paper_authors.fullname: Yuhui Fu
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paper_authors.name: Bohan Zhou
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paper_authors.name: Yicheng Feng
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paper_authors.fullname: yicheng feng
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paper_authors.user: takenpeanut
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paper_authors.name: Xinrun Xu
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paper_authors.fullname: Xinrun Xu
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paper_authors.user: SherryXu
paper_authors._id: 67d8eadc045f869fea1ce3f8
paper_authors.name: Yi Zhan
paper_authors._id: 67d8eadc045f869fea1ce3f9
paper_authors.name: Börje F. Karlsson
paper_authors.status: claimed_verified
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paper_authors.fullname: Börje Karlsson
paper_authors.type: user
paper_authors.user: tellarin
paper_authors._id: 67d8eadc045f869fea1ce3fa
paper_authors.name: Zongqing Lu
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/26712594ac9d43c8d1a3e75e36b5df16.svg
paper_authors.fullname: Zongqing Lu
paper_authors.type: user
paper_authors.user: chungtsing
paper_title: Being-0: A Humanoid Robotic Agent with Vision-Language Models and
    Modular Skills
paper_summary: Building autonomous robotic agents capable of achieving human-level
  performance in real-world embodied tasks is an ultimate goal in humanoid robot
  research. Recent advances have made significant progress in high-level
  cognition with Foundation Models (FMs) and low-level skill development for
  humanoid robots. However, directly combining these components often results in
  poor robustness and efficiency due to compounding errors in long-horizon tasks
  and the varied latency of different modules. We introduce Being-0, a
  hierarchical agent framework that integrates an FM with a modular skill
  library. The FM handles high-level cognitive tasks such as instruction
  understanding, task planning, and reasoning, while the skill library provides
  stable locomotion and dexterous manipulation for low-level control. To bridge
  the gap between these levels, we propose a novel Connector module, powered by a
  lightweight vision-language model (VLM). The Connector enhances the FM's
  embodied capabilities by translating language-based plans into actionable skill
  commands and dynamically coordinating locomotion and manipulation to improve
  task success. With all components, except the FM, deployable on low-cost
  onboard computation devices, Being-0 achieves efficient, real-time performance
  on a full-sized humanoid robot equipped with dexterous hands and active vision.
  Extensive experiments in large indoor environments demonstrate Being-0's
  effectiveness in solving complex, long-horizon tasks that require challenging
  navigation and manipulation subtasks. For further details and videos, visit
  https://beingbeyond.github.io/being-0.
paper_ai_summary: Being-0, a hierarchical framework integrating Foundation Models with a modular skill library and a lightweight vision-language model, achieves efficient real-time performance on complex humanoid robot tasks by coordinating high-level cognition with low-level control.
paper_ai_keywords: Foundation Models hierarchical agent framework modular skill library lightweight vision-language model vision-language model Connector module