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.

Jiatao Ding

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

Jiatao worked as an assistant research scientist at the Chinese University of Hong Kong (Shenzhen) from 2020 to 2022. From 2022 to 2025, he worked as a Postdoc Research Fellow in Cognitive Robotics at TU Delft, NL. Now, he works as a Research Fellow in the Department of Industrial Engineering at the University of Trento. His research interests include system modelling and control, optimal control, robot learning and their application to robotics, such as humanoids and quadrupeds.

Jiatao Ding | IDRA Labs

INVERSE

Vision

Challenges

Solution

Jiatao Ding