Imagine a robot that can teach itself to handle a sudden change in its surroundings without breaking or needing a human to rewrite its code. Instead of just slowing down or stopping when something unexpected happens, it finds a new way to work while staying completely safe. It is like a driver who can instantly adapt to a sudden road collapse without risking a crash.
Robots often fail or enter 'safe mode' (degraded functionality) when they encounter environments or structural changes they weren't programmed for. This leads to costly downtime and requires expensive manual reprogramming to restore performance.
A set of 'first time right' design tools and robotic platforms. This includes a deployment platform with lab-scale validation plans for trustworthy software adaptation.
If you are a warehouse automation provider dealing with unpredictable floor layouts or shifting inventory — this project developed a warehouse robotic swarm system that adapts its behavior while maintaining safety and performance. This ensures operations continue without manual reprogramming during environmental changes.
If you are an autonomous ship manufacturer dealing with unpredictable sea conditions or hull damage — this project developed a system for a prolonged hull of an autonomous vessel that adapts to structural changes. This keeps the vessel operational and safe even after unprecedented system changes.
If you are an industrial recycling plant dealing with varied and unpredictable waste materials — this project developed an industrial disassembly robot that adapts its software in real-time. This allows the robot to handle new object types without compromising safety or efficiency.
Based on available project data, no pricing or cost information is provided as this is a research and innovation action.
The project aims to validate and demonstrate the technology up to TRL4, meaning it is currently at a lab-scale validation stage rather than full industrial scale.
Based on available project data, specific licensing terms are not mentioned, though it involves a consortium of 10 partners including universities and SMEs.
It uses a combination of deep learning and MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) architectures to create generic adaptation procedures for robotic software.
The project period runs from 2024-01-01 to 2026-12-31, with the first version of the deployment platform and lab-scale validation plans as a key deliverable.
The consortium is heavily research-driven, consisting of 5 universities and 3 research organizations, with only 20% industry representation (2 SMEs). This suggests the output is currently focused on foundational software and design tools rather than immediate commercial products, though the inclusion of SMEs indicates a path toward industrial application.
Contact Aarhus Universitet (DK) regarding the RoboSAPIENS project coordination.
Contact us to identify potential licensing opportunities for TRL4 robotic adaptation tools.
