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Safe Multi-Agent Reinforcement Learning for Behavior-Based Cooperative Navigation
Demonstration-Enhanced Adaptive Multi-Objective Robot Navigation
Spatiotemporal Attention Enhances Lidar-Based Robot Navigation in Dynamic Environments
Ensemble Policies for Diverse Query-Generation in Preference Alignment of Robot Navigation
Subgoal-Driven Navigation in Dynamic Environments Using Attention-Based Deep Reinforcement Learning
Learning Depth Vision-Based Personalized Robot Navigation From Dynamic Demonstrations in Virtual Reality
Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Study
A Dynamic Safety Shield for Safe and Efficient Reinforcement Learning of Navigation Tasks
Enhanced Spatial Attention Graph for Motion Planning in Crowded, Partially Observable Environments
Nico Sven Ostermann-Myrau
Evaluating Robot Influence on Pedestrian Behavior Models for Crowd Simulation and Benchmarking
deheuvel
Agrawal
dawood
khorshidi
Amani
Shokry
Huang
Anticipating Human Behavior
This project focuses on creating technology for applications that predict human behavior. It covers a wide scope, including timeframes from milliseconds to hours and various levels of detail, from specific motions to general actions. The aim is to develop a comprehensive framework that doesn't isolate subproblems but integrates all aspects, allowing for accurate anticipation of human behavior, from long-term activity patterns to short-term detailed movements.
Motion Planning and Navigation in Dynamic Environments
With robots, be it mobile bases, quadrupeds, humanoids, or mobile manipulators, being deployed in unstructured and constantly changing environments, we focus on developing algorithms for online reactive motion planning and obstacle avoidance.
PRIVATAR - Privacy-friendly Mobile Avatars for Sick School Children
In order to promote the integration of acutely and chronically ill school children, the use of mobile robots as avatars at school offers a promising approach. Nevertheless, the robots, through their interactions and sensors, can endanger the different privacy dimensions of different people. PRIVATAR therefore aims to provide user-friendly solutions that allow users to better protect their privacy according to their own preferences through novel interactions. This gives them more control over their privacy, which goes far beyond the currently used consent forms.