INVERSE

INteractive robots that intuitiVely lEarn to inVErt tasks by ReaSoning about their Execution.

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.

Matteo Saveriano
Matteo Saveriano
Associate Professor

My research attempts to integrate cognitive robots into smart factories and social environments through the embodiment of AI solutions, inspired by the human behavior, into robotic devices.

Daniele Fontanelli
Daniele Fontanelli
Full Professor of Measurements and Robotics

Passionate researcher in distributed measurements for robotics application in the field of manufacturing, healthcare and agrifood.

Luigi Palopoli
Luigi Palopoli
Full Professor in Robotics

Researcher in the application of AI to social and rehabilitation robotics

Marco Roveri
Marco Roveri
Associate Professor

Passionate researcher in Planning, Scheduling, Formal Methods, and their application in the real world.

Mohammad H. Yeganegi
Mohammad H. Yeganegi
PhD Student

PhD Student in optimal control and machine learning