SOURCE-LINKED INTELLIGENCE

Robotic Safe Adaptation In unprecedented Situations

CORDIS · observation · Publication date unknown

o unprecedented system structural and environmental changes. 2. It will advance safety engineering techniques to assure robotic safety not only before, during and after adaptation. 3. It will advance deep learning techniques to actively reduce uncertainty in robotic self-adaptation. 4. It will assure trustworthiness of systems that use both deep-learning and computational architectures for robotic self-adaptation. To realise these objectives, RoboSAPIENS will extend techniques such as MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) and Deep Learning to set up generic adaptation procedures and also use an SSH dimension. RoboSAPIENS will demonstrate this trustworthy robotic self-adaptation on four industry-scale use cases centered around an industrial disassembly robot, a warehouse robotic swarm, a prolonged hull of an autonomous vessel, and human-robotic interaction. open-ended, uncertainty, control, safety, trustworthiness

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recordType

award

status

SIGNED

region

EU

value

6883233.5

unit

EUR