A ROS 2 Wrapper for Florence-2: Multi-Mode Local Vision-Language Inference for Robotic Systems
Summary: arXiv:2604.01179v1
Type: cross
Abstract: Foundation vision-language models are becoming increasingly relevant to robotics because they can provide richer semantic perception than narrow task-specific pipelines. However, their practical adoption in robot software stacks still depends on reproducible middleware integrations rather than on model quality alone. Florence-2 is especially attractive in this regard because it unifies captioning, optical character recognition, open-vocabulary detection, grounding and related vision-language tasks within a comparatively manageable model size.
This article presents a ROS 2 wrapper for Florence-2 that exposes the model through three complementary interaction modes: continuous topic-driven processing, synchronous service calls, and asynchronous actions. The wrapper is designed for local execution and supports both native installation and Docker container deployment. It also combines generic JSON outputs with standard ROS 2 message bindings for detection-oriented tasks.
Key Features of the ROS 2 Wrapper
- Multi-Mode Interaction: The wrapper supports three modes of interaction, allowing users to choose the most suitable method for their applications.
- Local Execution: The design prioritizes local execution, facilitating real-time processing for robotic systems.
- Deployment Flexibility: Users can deploy the wrapper using native installation methods or within Docker containers, ensuring compatibility across various environments.
- Output Formats: The wrapper produces generic JSON outputs while also adhering to standard ROS 2 message bindings, enhancing integration with existing robotic systems.
Performance and Validation
The performance of the wrapper has been validated through functional testing and throughput studies conducted on various GPU configurations. The results indicate that local deployment of Florence-2 is indeed feasible even with consumer-grade hardware. This makes advanced vision-language capabilities accessible to a broader range of robotic applications without the need for specialized or expensive computing resources.
Conclusion
The ROS 2 wrapper for Florence-2 represents a significant step forward in integrating sophisticated vision-language models into robotic systems. By providing a user-friendly interface and ensuring compatibility with existing ROS 2 frameworks, this development aims to enhance the semantic understanding and interaction capabilities of robots in diverse environments. The repository for this project is publicly available, encouraging further contributions and modifications from the community.
For those interested in exploring the ROS 2 wrapper for Florence-2, the code can be accessed on GitHub: GitHub Repository.