"INteractive robots that intuitiVely lEarn to inVErt tasks by ReaSoning about their Execution"
This is a research and innovation project funded by Horizon Europe.
The framework envisioned in INVERSE will result in substantial advances in long-term robot autonomy, enhancing the robot’s ability to solve complex manipulation tasks across different domains. Most learning algorithms fail to revert a learned task because they are not designed to adapt when training data are scarce or missing.
project focus
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.
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.
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.
