Università di Trento is coordinating an €8 million research initiative aimed at advancing the capabilities of intuitive and self-learning robots. This European-funded project focuses on developing robotic systems that can adapt and modify their behavior autonomously, without human intervention. Matteo Saveriano, leading the project, explains the central challenge: enabling robots to generate safe and sensible behavior even when operating outside their initial training data set.
The project, titled ‘Inverse’, is set to revolutionize tasks in both the automotive and heavy mechanical industries. In the automotive sector, the research will facilitate the assembly, disassembly, and recycling of electric vehicle batteries, allowing robots to autonomously adapt to tasks like battery installation and disassembly. In heavy industry, the project aims to improve safety and efficiency by automating overhead cranes, thus removing humans from hazardous and strenuous positions.
This initiative aligns with the European Union’s policies on circular and green economies, particularly in addressing the complex task of recycling battery components in the automotive industry. The Inverse project intends to automate this process, employing innovative solutions and flexible learning techniques tailored to varied battery components.
Furthermore, these robotic systems will contribute to sustainability by measuring energy efficiency, greenhouse gas emissions, material usage, and recycling rates, aiding in the disposal of industrial waste. The overarching goal is to demonstrate the efficiency of reprogramming existing robotic devices for new tasks, as opposed to training new ones from scratch.
The collaboration involves ten partners, including universities, research institutes, and industrial sector contributors. Set to commence in January, the project aims to create the first laboratory-tested robot prototype simulating an industrial environment by 2027.
