Customise Consent Preferences

We use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.

The cookies that are categorised as "Necessary" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... 

Always Active

Necessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.

No cookies to display.

Functional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.

No cookies to display.

Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.

No cookies to display.

Performance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors.

No cookies to display.

Advertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns.

No cookies to display.

Teaching Robots How to Pick Fruit

At this year’s European Robotics Forum, our partner Eurecat was a finalist for Best Paper with a smart, hands-on approach to one of agriculture’s biggest challenges: teaching robots to handle fruit harvesting with the same care and skill as a human.

Fruit picking might sound simple, but it’s incredibly hard to automate. Each apple or pear is slightly different and a farmer knows how to judge ripeness, adjust grip, and twist just the right way to avoid bruising the fruit. Keeping this in mind, Eurecat’s team developed an affordable, handheld device that helps capture those exact movements. Instead of complex lab setups, their system allows people to demonstrate natural harvesting actions right in the orchard.

What makes this hand-held gripper stand out is its simplicity and practicality. The device records a single video of someone picking multiple fruits, then uses smart software to automatically break that down into separate learning moments for the robot. It also combines sensors and external cameras to track the hand’s movements with accuracy, even in messy, unpredictable outdoor environments.

The end result is a low-cost tool that brings human expertise directly into robotic training. Eurecat tested the system in a real apple orchard, and the robot was able to reproduce picking movements much more accurately than before. The team’s work brings us closer to a future where robots can support farmers by taking on repetitive or physically demanding tasks—without losing the human touch.