Finding the Easy Way Through – the Probabilistic Gap Planner for Social Robot Navigation
Probabilistic gap planner which improves robot navigation by guiding short-term planners towards low-density crowd regions.
I am a researcher in motion planning for mobile robots. I am an associate at Honda Research Institute Japan and Europe where we are pushing the intelligence and cooperative interaction of robots in human environments. I received my Ph.D from the Technical University of Darmstadt in 2021 where I was advised by Julian Eggert and Jürgen Adamy.
My current research interests are primarily in robust planning, cooperative planning and human-robot interaction.
Probabilistic gap planner which improves robot navigation by guiding short-term planners towards low-density crowd regions.
An analytic behavior planner enables autonomous vehicles to generate high-quality decisions without relying on predefined behavior primitives.
Cooperative ordering resolved by switching-aware negotiation to handle dynamic intent in urban mobility scenarios.
Cooperative behavior planning using asymmetry-based outcome prediction to navigate narrow passages without communication.
Assistive device for the vision impaired using uncertainty-aware motion prediction for surrounding objects.
Human-robot interaction using human state detection and behavior models to anticipate errors and provide adaptive support.