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Pollen-Vision: Unified interface for Zero-Shot vision models in robotics
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Pollen-Vision: Unified interface for Zero-Shot vision models in robotics

This is a guest blog post by the Pollen Robotics team. We are the creators of Reachy, an open-source humanoid robot designed for manipulation in the real world.

In the context of autonomous behaviors, the essence of a robot's usability lies in its ability to understand and interact with its environment. This understanding primarily comes from visual perception, which enables robots to identify objects, recognize people, navigate spaces, and much more.

We're excited to share the initial launch of our open-source pollen-vision library, a first step towards empowering our robots with the autonomy to grasp unknown objects. This library is a carefully curated collection of vision models chosen for their direct applicability to robotics. Pollen-vision is designed for ease of installation and use, composed of independent modules that can be combined to create a 3D object detection pipeline, getting the position of the objects in 3D space (x, y, z).

We focused on selecting zero-shot models, eliminating the need for any training, and making these tools instantly usable right out of the box.

Our initial release is focused on 3D object detection—laying the groundwork for tasks like robotic grasping by providing a reliable estimate of objects' spatial coordinates. Currently limited to positioning within a 3D space (not extending to full 6D pose estimation), this functionality establishes a solid foundation for basic robotic manipulation tasks.

The Core Models of Pollen-Vision

The library encapsulates several key models. We want the models we use to be zero-shot and versatile, allowing a wide range of detectable objects without re-training. The models also have to be “real-time capable”, meaning they should run at least at a few fps on a consumer GPU. The first models we chose are:

Get started in very few lines of code!

Below is an example of how to use pollen-vision to build a simple object detection and segmentation pipeline, taking only images and text as input.

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