Imagine a robot that doesn't just freeze when it hits a wall or finds a door closed, but actually thinks and finds a new way to finish its job. It's like giving a robot a 'common sense' brain that learns from its mistakes. This ensures the machine stays safe and keeps working even in messy, unpredictable real-world settings.
Robots currently fail in unstructured environments when they encounter unexpected situations, requiring expensive human intervention to reset or fix them.
A model-driven software toolchain for designing robot deliberation systems. It includes tools for automatic verification of safety and security at design and run-time.
If you are a home appliance manufacturer dealing with vacuum robots that get stuck and require manual rescue — this project developed a toolchain that allows robots to learn from experience and avoid getting stuck. This reduces customer frustration and product returns.
If you are an industrial automation provider dealing with assembly lines where parts are missing or lighting is poor — this project developed cognitive deliberation capabilities that help robots cope with occlusions and restrained parts. This minimizes production downtime.
If you are a museum operator dealing with unpredictable human crowds that confuse your guide robots — this project developed a system to handle problematic situations arising from human behavior. This ensures a smooth visitor experience without constant staff intervention.
Based on available project data, no specific pricing or licensing costs are mentioned, though parts of the toolchain will be open-sourced.
The project tests its technology on three real-world scenarios of increasing complexity, including assembly robots, suggesting a path toward industrial scaling.
The project states that major parts of the toolchain shall be open-sourced with adapters for the Robot Operating System (ROS).
Yes, the design process specifically scrutinizes requirements against the EU Artificial Intelligence Act, GDPR, and Ethical AI regulations.
The toolchain is designed with adapters for the Robot Operating System (ROS), which is a standard in the robotics community.
The consortium is well-balanced for commercialization, consisting of 10 partners across 6 countries. With a 40% industry ratio (4 companies, including 3 SMEs), there is a strong link between academic research and market application, ensuring the toolchain meets actual developer needs.
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