Nora-Long
Nora-Long is an open vision-language-action model trained on robot manipulation episodes from the Open X-Embodiment dataset. The model takes language instructions and camera images as input and generates robot actions. Nora-Lonf is trained directly from Qwen 2.5 VL-3B. All Nora checkpoints, as well as our training codebase are released under an MIT License.
Unlike Nora, Nora-Long is pretrained with an action horizon of 5. We observe worse performance on WidowX robot task with Nora-Long, but superior performance in libero simulation. Please feel free to finetune this model!
Model Description
- Model type: Vision-language-action (language, image => robot actions)
- Language(s) (NLP): english
- License: MIT
- Finetuned from model : Qwen 2.5 VL-3B
Model Sources
- Repository: https://github.com/declare-lab/nora
- Paper : https://www.arxiv.org/abs/2504.19854
- Demo: https://declare-lab.github.io/nora
Usage
Nora take a language instruction and a camera image of a robot workspace as input, and predict (normalized) robot actions consisting of 7-DoF end-effector deltas of the form (x, y, z, roll, pitch, yaw, gripper). To execute on an actual robot platform, actions need to be un-normalized subject to statistics computed on a per-robot, per-dataset basis. Instructions on how to run Nora is available on https://github.com/declare-lab/nora.