INteractive robots that intuitiVely lEarn to inVErt tasks by ReaSoning about their Execution (INVERSE)

Recent developments in Artificial Intelligence (AI) have only partially increased robots’ autonomy and their capability to solve manipulation tasks. Although robots are now allowed to work in close proximity to humans, they lack the cognitive capabilities needed to support more sophisticated interactions with human co-workers and the environment. Progress in robot autonomy is slow, and AI technological solutions underlying current robotic platforms are not yet mature enough to understand how to perform a learned task in shifted, albeit similar domains.

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. INVERSE’s approach to synthesise reusable and robust plans implies an enhanced ability of the robot

  • to understand its surroundings, including human intentions and needs, in order to determine what action the robot has to perform and how the environment is expected to change as a result;
  • to represent robot knowledge in a flexible structure, specifically designed to facilitate execution monitoring, recovery from failures and task inversion;
  • to adapt robot knowledge to different domains in order to synthesise effective solutions for direct and inverse tasks and to robustly react to the intrinsic variability of non-repetitive tasks.

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.

Further information:

  • Funding: European Commission, Horizon Europe
  • EU Funding for all partners: 7,9 million EUR
  • Project duration: 01/2024 – 12/2027
  • Participating countries: Austria, Estonia, Finland, Germany, Italy, Spain, Turkey

Further links


LinkedIn: INVERSE (EU Project): Overview | LinkedIn

Keywords: Artificial Intelligence, Convolutional Neural Network, Human-Robot Collaboration, Model Predictive Control, Robot Operating System, eXplainable AI

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Ivo Zeller
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Ivo Zeller

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