About

Advancements in deep learning and robotics have made it possible to automate complex dexterous tasks that were previously out of reach. BluPe is leveraging these breakthroughs to create robotic solutions for repetative manipulation tasks, starting with blueberries.

How It Works

A single arm is equipped with four cameras and real-time feedback on motor positions. Based on this data, it predicts how the robotic arm needs to move. By sampling movements and learning from demonstrations, the system refines its ability to pick fruit efficiently.

In 2018, advancements in deep learning enabled models to predict long sequences of actions, making complex robotic control possible. By 2023, this architecture was successfully applied to imitation learning, reducing the cost of robotics by nearly 10x. This type of model is now used in advanced humanoid robots like Tesla’s Optimus and other dexterous systems.

The Longtail problem

While the industry is pushing for generalizable robotics, certain tasks require specialized solutions. Humanoid robots are versatile but not always optimal—particularly in time-sensitive applications like blueberry picking, where speed is critical. BluPe is focused on solving these long-tail problems where traditional robotics fall short.

Ethical Considerations

As with any technological advancement, we understand that the automation of tasks will undoubtably replace existing jobs while creating opportunities in other areas. We are committed to deploying this technology responsibly, ensuring a smooth transition for workers while improving efficiency in the agricultural sector.

BluPe starts with blueberry harvesting but aims to expand into other high-value tasks that demand precision and speed. Stay tuned for more updates on our journey.