What unique contributions does AI2Like bring to the PILLAR consortium, and how do you see its impact on the agri-food robotics sector? How will the project’s efforts benefit stakeholders in this field who are following these developments?

AI2Life Srl (AI2L) is a spin-off company of ISTC-CNR that plays a key role in the PILLAR-Robots project, particularly in the agri-food application domain. Their unique contributions include:

1. Technology Transfer Role:AI2L serves as a bridge between basic research and industry applications, helping to transform the theoretical aspects of PILLAR-Robots’ cognitive architecture into practical solutions for the agri-food sector.
2. Application Expertise: In WP7, AI2L takes the lead in implementing and validating the project’s technology in post-harvesting agri-food scenarios, focusing specifically on picking, sorting, and packaging activities for fruits and vegetables.
3. Network of Connections: The document notes that AI2L contributes to the project by “exploiting a network that involves research institutes, universities, start-ups, public and private institutes, high-tech suppliers and businesses,” with specific “contacts in the Italian agri-food market.”

KPI Definition and Validation: AI2L leads Task 7.1, defining key performance indicators with stakeholders, and Task 7.4, focusing on technology assessment and validation in real agri-food scenarios.

The hackathon activities, featuring PAL Robotics’ TIAGo robot, spanned three intensive days in Athens at the latest project meeting. How do you envision the future advancements of this technology, and in what ways has this collaborative meeting helped the project move closer to achieving a fully autonomous robotic solution for agri-food applications?

The project meeting in Athens was a crucial opportunity for partners to share ideas and define next steps in developing PILLAR-Robots into a fully autonomous system for agri-food applications. During the hackathon featuring PAL Robotics’ TIAGo robot, new architectural concepts were proposed to test the open-ended learning process better. The team plans to introduce distractor objects and sequential operations necessary to complete tasks provided to the robot through user-defined purposes.

These innovations will be progressively integrated into the project, specifically adapted to the agri-food context. The validation of the proposed environment will occur with stakeholder involvement, enabling direct feedback on sector needs and ensuring that solutions developed during the Athens meeting are practically applicable in industrial settings.

Tell us a bit more about yourself, your organisation, and your role in the PILLAR project

AI2Life brings valuable expertise from previous European projects, including GROW (General-purpose Robots for Object-retrieval Warehouses), which focused on autonomous learning technologies for logistics – experience that transfers well to the agri-food sector.

AI2Life’s interdisciplinary team combines expertise in AI, deep learning, computer vision, autonomous robotics, and applications in non-engineered environments – all critical for developing robots capable of handling the high variability of fruits and vegetables in post-harvesting processes.

In the PILLAR-Robots project, AI2Life embodies the core principle of bringing “AI Solutions Out of the Lab” – exactly what’s needed to transform the theoretical advances of purposeful intrinsically-motivated lifelong learning into practical solutions for the agri-food industry.