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deheuvel

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Agrawal

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dawood

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khorshidi

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Amani

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Shokry

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Huang

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Nico Sven Ostermann-Myrau

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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.
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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.
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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.
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A Dynamic Safety Shield for Safe and Efficient Reinforcement Learning of Navigation Tasks

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Safe Multi-Agent Reinforcement Learning for Behavior-Based Cooperative Navigation

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Demonstration-Enhanced Adaptive Multi-Objective Robot Navigation

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Handling Sparse Rewards in Reinforcement Learning Using Model Predictive Control

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Subgoal-Driven Navigation in Dynamic Environments Using Attention-Based Deep Reinforcement Learning

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Learning Depth Vision-Based Personalized Robot Navigation From Dynamic Demonstrations in Virtual Reality

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Spatiotemporal Attention Enhances Lidar-Based Robot Navigation in Dynamic Environments

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Enhanced Spatial Attention Graph for Motion Planning in Crowded, Partially Observable Environments

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Sensor-Based Navigation Using Hierarchical Reinforcement Learning

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Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Study

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Fast-Replanning Motion Control for Non-Holonomic Vehicles with Aborted A*

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3D Polygonal Mapping for Humanoid Robot Navigation

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The Pitfall of More Powerful Autoencoders in Lidar-Based Navigation

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Human-Aware Robot Navigation Based on Learned Cost Values from User Studies

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Contact:

Prof. Dr. Maren Bennewitz
Humanoid Robots Lab
University of Bonn
Friedrich-Hirzebruch-Allee 8
53115 Bonn

Tel.: +49 (0) 228 73-54164
Secr.: +49 (0) 228 73-4422
Fax.: +49 (0) 228 73-4425
Email: maren_at_cs.uni-bonn.de

Office: 2.022

University of Bonn
Institute for Computer Science
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