title: NORA: A Small Open-Sourced Generalist Vision Language Action Model for
    Embodied Tasks
summary: Existing Visual-Language-Action (VLA) models have shown promising performance
  in zero-shot scenarios, demonstrating impressive task execution and reasoning
  capabilities. However, a significant challenge arises from the limitations of
  visual encoding, which can result in failures during tasks such as object
  grasping. Moreover, these models typically suffer from high computational
  overhead due to their large sizes, often exceeding 7B parameters. While these
  models excel in reasoning and task planning, the substantial computational
  overhead they incur makes them impractical for real-time robotic environments,
  where speed and efficiency are paramount. To address the limitations of
  existing VLA models, we propose NORA, a 3B-parameter model designed to reduce
  computational overhead while maintaining strong task performance. NORA adopts
  the Qwen-2.5-VL-3B multimodal model as its backbone, leveraging its superior
  visual-semantic understanding to enhance visual reasoning and action grounding.
  Additionally, our  is trained on 970k real-world robot demonstrations
  and equipped with the FAST+ tokenizer for efficient action sequence generation.
  Experimental results demonstrate that NORA outperforms existing large-scale VLA
  models, achieving better task performance with significantly reduced
  computational overhead, making it a more practical solution for real-time
  robotic autonomy.
paper_authors._id: 6810b130e4e00252c5f76728
paper_authors.name: Chia-Yu Hung
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-05T14:07:13.321Z
paper_authors._id: 62864cdf011a13fe5f84d746
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62864cdf011a13fe5f84d746/5yPYgAtGJTaUq4fHhh9Pr.jpeg
paper_authors.fullname: Hung Chia Yu
paper_authors.type: user
paper_authors.user: hungchiayu
paper_authors._id: 6810b130e4e00252c5f76729
paper_authors.name: Qi Sun
paper_authors._id: 6810b130e4e00252c5f7672a
paper_authors.name: Pengfei Hong
paper_authors._id: 6810b130e4e00252c5f7672b
paper_authors.name: Amir Zadeh
paper_authors._id: 6810b130e4e00252c5f7672c
paper_authors.name: Chuan Li
paper_authors._id: 6810b130e4e00252c5f7672d
paper_authors.name: U-Xuan Tan
paper_authors._id: 6810b130e4e00252c5f7672e
paper_authors.name: Navonil Majumder
paper_authors._id: 6810b130e4e00252c5f7672f
paper_authors.name: Soujanya Poria
paper_title: NORA: A Small Open-Sourced Generalist Vision Language Action Model for
    Embodied Tasks
paper_summary: Existing Visual-Language-Action (VLA) models have shown promising performance
  in zero-shot scenarios, demonstrating impressive task execution and reasoning
  capabilities. However, a significant challenge arises from the limitations of
  visual encoding, which can result in failures during tasks such as object
  grasping. Moreover, these models typically suffer from high computational
  overhead due to their large sizes, often exceeding 7B parameters. While these
  models excel in reasoning and task planning, the substantial computational
  overhead they incur makes them impractical for real-time robotic environments,
  where speed and efficiency are paramount. To address the limitations of
  existing VLA models, we propose NORA, a 3B-parameter model designed to reduce
  computational overhead while maintaining strong task performance. NORA adopts
  the Qwen-2.5-VL-3B multimodal model as its backbone, leveraging its superior
  visual-semantic understanding to enhance visual reasoning and action grounding.
  Additionally, our  is trained on 970k real-world robot demonstrations
  and equipped with the FAST+ tokenizer for efficient action sequence generation.
  Experimental results demonstrate that NORA outperforms existing large-scale VLA
  models, achieving better task performance with significantly reduced
  computational overhead, making it a more practical solution for real-time
  robotic autonomy.
paper_ai_summary: NORA is a 3B-parameter VLA model using Qwen-2.5-VL-3B for efficient visual reasoning and action grounding, outperforming larger models with reduced computational overhead.
paper_ai_keywords: Visual-Language-Action (VLA) models visual encoding multimodal model visual-semantic understanding real-world robot demonstrations FAST+ tokenizer action sequence generation