In the field of artificial intelligence, the Vision-Language- Action ( VLA ) model is gradually becoming a key technology that connects human language with robot actions. With the continuous development of robotics, how to make robots better understand human language instructions and convert them into precise actions has become a hot topic of research . Recently, research teams from Stanford University and other institutions have open-sourced  the OpenVLA  model, which has brought new hope to the development of robotics with its efficient parameter utilization and excellent performance. This article will introduce in detail  the project background, technical principles, functional features, application scenarios, and how to quickly get started with OpenVLA  , to help readers fully understand this cutting-edge technology.

1. Project Overview

OpenVLA  was developed by a research team from Stanford University and other institutions. It is dedicated to building an open source visual language action model system. Its core idea is to use a large-scale pre-trained model architecture to integrate massive Internet-scale visual language data and a variety of actual robot demonstration data to enable robots to quickly master new skills. The core goal of the project focuses on enabling robots to quickly adapt to new tasks and complex and changing environments through efficient parameter fine-tuning strategies, thereby significantly improving the robot's generalization ability and overall performance , and promoting robots from single-task execution to flexible response to complex scenarios.

2. Technical Principle

1. Model structure

OpenVLA  is based on a  7B  parameter  Llama 2  language model, combined with  a visual encoder that incorporates DINOv2  and  SigLIP  pre-trained features. This structure enables the model to better process visual and language information, thereby generating more accurate robot actions. Specifically, the visual encoder is responsible for processing input image data and extracting visual features; the language model is responsible for processing natural language instructions and understanding the semantics of the instructions. After combining the two, the model can convert language instructions into specific robot actions.

2. Training Data

OpenVLA  was trained on 970,000 real-world robot demonstrations from the Open X-Embodiment dataset. These  data  cover  a  variety of tasks and scenarios, providing a rich learning resource for the model. By training on a large-scale dataset, OpenVLA  is able to learn common features of different tasks, thereby improving its generalization ability.

3. Training methods

OpenVLA  uses a parameter-efficient fine-tuning method that allows the model to quickly adapt to new robotics domains. This fine-tuning method not only improves the adaptability of the model, but also reduces the training time and computing resource requirements. In addition, OpenVLA  also supports fine-tuning on consumer-grade  GPUs  and achieves efficient service through quantization.

3. Features

1. Efficiency

Compared to existing closed-loop models (e.g., RT-2-X , 55B  parameters), OpenVLA  achieves 16.5% higher absolute task success rate across  29  tasks and multiple robot implementations  , while using 7x  fewer parameters  . This demonstrates that  OpenVLA  has better generalization and performance while maintaining high efficiency. 

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2. Powerful generalization capability

OpenVLA  performs well in multi-task environments involving multiple objects and strong language foundation capabilities. This shows that the model is not only capable of handling single tasks, but also maintains high performance in complex multi-task scenarios.

3. Open Source

OpenVLA  ’s model checkpoints, fine-tuning notebooks, and  PyTorch  training pipelines are all fully open source , which means researchers and developers can freely access and use these resources to accelerate the development of robotics.

IV. Application Scenarios

1. Home service robots

In a home environment, OpenVLA  can significantly improve the intelligence level of service robots. The robot can accurately understand the user's voice commands, such as "clean the bedroom floor, clear the debris on the sofa", and with the help of  OpenVLA's  powerful vision and language processing capabilities, it can identify the bedroom boundary, floor area, sofa and debris, plan a reasonable cleaning path, and accurately perform cleaning and tidying actions, creating a more convenient and comfortable home life experience for users.

2. Industrial Robots

On industrial production lines, OpenVLA  helps robots quickly adapt to the production needs of new products and new processes. When new parts assembly tasks are introduced, through efficient parameter fine-tuning, robots can quickly understand the language description of the assembly process, combine visual recognition of parts features and positions, and quickly get started with new tasks , greatly shortening the production line adjustment cycle, improving production efficiency and flexibility, and providing strong support for industrial enterprises to cope with rapidly changing market demands.

3. Education and Research

The open source nature of OpenVLA  makes it an ideal tool for education and research. In the teaching of relevant courses in colleges and universities, students can conduct experiments such as robot vision language interaction and motion planning based on  OpenVLA  model resources to deepen their understanding and practical ability of robotics technology . Researchers can also use its model checkpoints and training pipelines to explore the application of robots in emerging fields such as medical rehabilitation and disaster relief, and promote the continuous expansion of the boundaries of robotics technology.

5. Quick Use

1. Environmental preparation

Before you begin, make sure you have installed the following necessary software and libraries:

- Python 3.10 (recommended version)

- PyTorch 2.2.0

- OpenVLA  code base

( II ) Installation Dependencies

1.  Create  a Python  environment and install  PyTorch

conda create -n openvla python=3.10 -yconda activate openvlaconda install pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia -y

2.  Install  the OpenVLA  code library

git clone https://github.com/openvla/openvla.gitcd openvlapip install -e .

3.  Install  Flash Attention 2 (for training):

pip install packaging ninjaninja --version; echo $? # Verify Ninja --> should return exit code "0"pip install "flash-attn==2.5.5" --no-build-isolation

( III ) Code Examples

Here is the sample code for loading the `openvla-7b`  model for zero-shot instruction following: 

from  transformers  import  AutoModelForVision2Seq, AutoProcessorfrom  PIL  import  Imageimport  torch
# Load Processor & VLAprocessor = AutoProcessor.from_pretrained( "openvla/openvla-7b" , trust_remote_code= True )vla = AutoModelForVision2Seq.from_pretrained(    "openvla/openvla-7b" ,    attn_implementation= "flash_attention_2" ,   # [Optional] Requires `flash_attn`    torch_dtype=torch.bfloat16,    low_cpu_mem_usage = True ,    trust_remote_code= True).to( "cuda:0" )
# Grab image input & format promptimage: Image.Image = get_from_camera(...)   # Replace with your image inputprompt =  "In: What action should the robot take to {<INSTRUCTION>}?\nOut:"
# Predict Action (7-DoF; un-normalize for BridgeData V2)inputs = processor(prompt, image).to( "cuda:0" , dtype=torch.bfloat16)action = vla.predict_action(**inputs, unnorm_key= "bridge_orig" , do_sample= False )
# Execute...robot.act(action, ...)   # Replace with your robot execution code

For more information, please refer to the OpenVLA GitHub  repository (https://github.com/openvla/openvla)

VI. Conclusion

As an open-source visual language action model from Stanford University and other institutions, OpenVLA  has brought new hope to the development of robotics with its efficient parameter utilization and excellent performance. Through pre-trained large models and training of large-scale data sets, OpenVLA  can quickly adapt to new tasks and environments and improve the generalization ability of robots. At the same time, its open source nature also provides researchers and developers with rich resources, accelerating the development of robotics.