INVERSE

Mon, 01 Jan 2024 00:00:00 +0000

This is a research and innovation project funded by Horizon Europe. Website: https://www.inverse-project.org/.

4-year EU project: January 2024-December 2027
12 consortium partners from 8 countries across Europe coordinated by Università di Trento
2 complementary use cases designed to be a realistic instantiation of the actual work environments

Vision

The scientific vision of INVERSE is to endow robots with the cognitive capabilities needed to synthesise, monitor, and execute inverse plans from direct tasks defined in terms of human-understandable instructions and procedures.

Challenges

Recent advancements in Artificial Intelligence (AI) have improved robot autonomy and manipulation tasks but fall short in enabling sophisticated interactions with humans and adapting to new environments. While robots can now operate closer to humans, they lack the necessary cognitive capabilities to understand and execute tasks in varied domains, similar to human adaptability and problem-solving.

Solution

The INVERSE project aims to provide robots with these essential cognitive abilities by adopting a continual learning approach. After an initial bootstrap phase, used to create initial knowledge from human-level specifications, the robot refines its repertoire by capitalising on its own experience and on human feedback. This experience-driven strategy permits to frame different problems, like performing a task in a different domain, as a problem of fault detection and recovery. Humans have a central role in INVERSE, since their supervision helps limit the complexity of the refinement loop, making the solution suitable for deployment in production scenarios. The effectiveness of developed solutions will be demonstrated in two complementary use cases designed to be a realistic instantiation of the actual work environments.

Leonardo Rossi

Mon, 01 Jan 2024 00:00:00 +0000

Leonardo Rossi is a PhD Student in Material, Mechatronics and Systems Engineering at the University of Trento. His research focuses on robot learning, with particular emphasis on Reinforcement Learning and Foundation Models for robotics. A central aspect of his work is the development of methods that aim to enhance safety, robustness to irreversible events, and reliability of robotic systems in real-world environments.

Leonardo Rossi | IDRA Labs

INVERSE

Vision

Challenges

Solution

Leonardo Rossi