nora-1.5

NORA-1.5: A Vision-Language-Action Model Trained using World Model- and Action-based Preference Rewards

🔥 Project NORA is supported by Gemini and Lambda Labs! We are thankful to them.

NORA-1.5 is a Vision-Language-Action (VLA) model that improves generalization and real-world decision making through post-training with world-model-based and action-based preference rewards.
The model builds upon the NORA foundation to achieve stronger instruction following, closed-loop control, and real-robot success, demonstrating reliability across LIBERO and SimplerEnv environments.

This repository consolidates the full open-source release of model checkpoints, inference code, training code, and evaluation tools, along with documentation and examples.

<p align="center"> <img src="https://declare-lab.github.io/assets/images/nora-1.5-arxiv-teaser.png" width="100%"> </p>

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🌐 Project Website

🔗 https://declare-lab.github.io/nora-1.5

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🚀 Key Features

• Vision-Language-Action architecture with enhanced task completion rate and distraction rate
• Action-based preference optimization using expert preference rewards
• World-model-based preference learning for improved planning and consistency
• Strong closed-loop control, enabling deployment in real robot settings
• Supports multi-task, long-horizon, and few-shot generalization
• Compatible with LeRobot, LIBERO, SimplerEnv, and custom environments

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📦 Repository Structure (will update)

📆 TODO <a name="todos"></a> ~ 1 week

• Release the inference code of Nora-1.5
• Release all relevant model checkpoints(Pretrained, libero, SimplerEnv etc)
• Release the training/fine-tuning code of Nora-1.5 with LeRobot Dataset
• Release SimplerEnv evaluation code

Minimal Inference Sample (Will update)

from inference.modelling_expert import VLAWithExpert

model = VLAWithExpert() 
model.to('cuda')
outputs = model.sample_actions(PIL IMAGE,instruction,num_steps=10) ## Outputs 7 Dof action of normalized and unnormalized action

Citation

@article{hung2025nora15,
  title={NORA-1.5: A Vision-Language-Action Model Trained using World Model- and Action-Based Preference Rewards},
  author={Hung, Chia-Yu and Majumder, Navonil and Deng, Haoyuan, Liu Renhang, Yankang Ang, Amir Zadeh, Chuan Li, Dorien Herremans, Ziwei Wang, and Soujanya Poria},
  journal={arXiv preprint},
  year={2025}
}