Deployable Decision-Making: Embracing Semantics for Robotic Safety in Everyday Scenarios

Informations projet

SSDM

N° de convention de subvention: 101155035

DOI

10.3030/101155035

Projet clôturé le 31 Août 2025

Date de signature de la CE 3 Mai 2024

Date de début 1 Juin 2024

Date de fin 31 Mai 2026

Financé au titre de

• Marie Skłodowska-Curie Actions (MSCA)

Coût total

Aucune donnée

Contribution de l’UE

€ 173 847,36

Coordonné par

TECHNISCHE UNIVERSITAET MUENCHEN
Germany

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Livrables

Data Management Plan (1)

Data Management Plan

The Data Management Plan describes the data management life cycle for all data sets that will be collected, processed or generated by the action. It is a document describing what data will be collected, processed or generated and following what methodology and standards, whether and how this data will be shared and/or made open, and how it will be curated and preserved.

Documents, reports (1)

Communication, Dissemination & Exploitation Plan

The plan describes the planned measures to maximize the impact of the project, including the dissemination and exploitation measures that are planned, and the target group(s) addressed. Regarding communication measures and public engagement strategy, the aim is to inform and reach out to society and show the activities performed, and the use and the benefits the project will have for citizens.

Publications

Peer reviewed articles (3)

Preventing Inactive CBF Safety Filters Caused by Invalid Relative Degree Assumptions

Auteurs: Lukas Brunke, Siqi Zhou, Angela P. Schoellig
Publié dans: IEEE Transactions on Automatic Control, 2025, ISSN 0018-9286
Éditeur: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/TAC.2025.3608258

Semantically Safe Robot Manipulation: From Semantic Scene Understanding to Motion Safeguards

Auteurs: Lukas Brunke, Yanni Zhang, Ralf Römer, Jack Naimer, Nikola Staykov, Siqi Zhou, Angela P. Schoellig
Publié dans: IEEE Robotics and Automation Letters, Numéro 10, 2025, ISSN 2377-3766
Éditeur: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/LRA.2025.3553046

SwarmGPT: Combining Large Language Models with Safe Motion Planning for Drone Swarm Choreography

Auteurs: Martin Schuck, Dinushka Orrin Dahanaggamaarachchi, Ben Sprenger, Vedant Vyas, Siqi Zhou, Angela P. Schoellig
Publié dans: IEEE Robotics and Automation Letters, 2025, ISSN 2377-3766
Éditeur: Institute of Electrical and Electronics Engineers
DOI: 10.1109/LRA.2025.3619745