Hello, I'm Tim Puphal

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.

Publications

Robust Planning

Finding the Easy Way Through – the Probabilistic Gap Planner for Social Robot Navigation

Finding the Easy Way Through – the Probabilistic Gap Planner for Social Robot Navigation

IEEE ROMAN, 2025

Probabilistic gap planner which improves robot navigation by guiding short-term planners towards low-density crowd regions.

Automated Driving in Complex Real-World Scenarios using a Scalable Risk-Based Behavior Generation Framework

Automated Driving in Complex Real-World Scenarios using a Scalable Risk-Based Behavior Generation Framework

IEEE ITSC, 2021

An analytic behavior planner enables autonomous vehicles to generate high-quality decisions without relying on predefined behavior primitives.

Cooperative Planning

Negotiating Cooperative Ordering Problems with Bimodal Planning

Negotiating Cooperative Ordering Problems with Bimodal Planning

IEEE IV, 2025

Cooperative ordering resolved by switching-aware negotiation to handle dynamic intent in urban mobility scenarios.

Asymmetry-based Behavior Planning for Cooperation at Shared Traffic Spaces

Asymmetry-based Behavior Planning for Cooperation at Shared Traffic Spaces

IEEE IV, 2021

Cooperative behavior planning using asymmetry-based outcome prediction to navigate narrow passages without communication.

Human-Robot Interaction

Probabilistic Collision Risk Estimation for Pedestrian Navigation

Probabilistic Collision Risk Estimation for Pedestrian Navigation

IEEE/RSJ IROS, 2025

Assistive device for the vision impaired using uncertainty-aware motion prediction for surrounding objects.

Considering Human Factors in Risk Maps for Robust and Foresighted Driver Warning

Considering Human Factors in Risk Maps for Robust and Foresighted Driver Warning

IEEE ROMAN, 2023

Human-robot interaction using human state detection and behavior models to anticipate errors and provide adaptive support.