NORA: A Small Open-Sourced Generalist Vision Language Action Model for
  Embodied Tasks
Authors: name: Chia-Yu Hung, name: Qi Sun, name: Pengfei Hong, name: Amir Zadeh, name: Chuan Li, name: U-Xuan Tan, name: Navonil Majumder, name: Soujanya Poria

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.