// COMPANY
TU Berlin
SECTOR RESEARCHMENTIONS 5LAST SEEN SEPTEMBER 16, 2026
// RECENT MENTIONS
// SIGNALS
5 SIGNALS
01
product·arXiv Physical AI·SEPTEMBER 16, 2026
“The paper introduces DynoFluxBench, a framework designed to test motion planners that must simultaneously respect a robot's physical dynamics (kinodynamic constraints) and avoid moving obstacles over time (space-time planning).”
Source→02
mention·arXiv Physical AI·SEPTEMBER 16, 2026
“there are no dedicated benchmark frameworks that combine both aspects”
Source→03
mention·arXiv Physical AI·SEPTEMBER 16, 2026
“Franz Queißner... Lead author of the paper and developer of the DynoFluxBench framework and the three new planners.”
Source→04
mention·arXiv Physical AI·SEPTEMBER 16, 2026
“Wolfgang Hönig... Co-author known for his work in multi-robot path planning and kinodynamic search (e.g., db-A*)”
Source→05
mention·arXiv Physical AI·SEPTEMBER 16, 2026
“his lab's prior work on iDb-RRT and Dynobench forms the foundation for the DynoFluxBench framework and the ST-Db-RRT planner”
Source→AI-extracted from podcast / newsletter / paper summaries. May contain errors.