NORA: A Small Open-Sourced Generalist Vision Language Action Model for
  Embodied Tasks
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.
6810b130e4e00252c5f76728
Chia-Yu Hung
claimed_verified
2025-05-05T14:07:13.321Z
62864cdf011a13fe5f84d746
https://cdn-avatars.huggingface.co/v1/production/uploads/62864cdf011a13fe5f84d746/5yPYgAtGJTaUq4fHhh9Pr.jpeg
Hung Chia Yu
user
hungchiayu
6810b130e4e00252c5f76729
Qi Sun
6810b130e4e00252c5f7672a
Pengfei Hong
6810b130e4e00252c5f7672b
Amir Zadeh
6810b130e4e00252c5f7672c
Chuan Li
6810b130e4e00252c5f7672d
U-Xuan Tan
6810b130e4e00252c5f7672e
Navonil Majumder
6810b130e4e00252c5f7672f
Soujanya Poria
NORA: A Small Open-Sourced Generalist Vision Language Action Model for
  Embodied Tasks
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.
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.
Visual-Language-Action (VLA) models visual encoding multimodal model visual-semantic understanding real-world robot demonstrations FAST+ tokenizer action sequence generation