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Embodied AI at LAMARR Institute for Machine Learning and Artificial Intelligence

The Lamarr Institute, emerging from the ML2R project after expert evaluation, is dedicated to advancing high-performance, reliable, and efficient Machine Learning and AI. Aiming to make Germany and Europe leaders in AI research, education, and technology transfer, it now enjoys permanent funding from the Federal Ministry of Education and Research and the state of North Rhine-Westphalia.

Safe Multi-Agent Reinforcement Learning for Formation Control Without Individual Reference Targets

Handling Sparse Rewards in Reinforcement Learning Using Model Predictive Control

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

Viewpoint Push Planning for Mapping of Unknown Confined Spaces

Spatiotemporal Attention Enhances Lidar-Based Robot Navigation in Dynamic Environments

Integrating One-Shot View Planning with a Single Next-Best View via Long-Tail Multiview Sampling

Ensemble Policies for Diverse Query-Generation in Preference Alignment of Robot Navigation

Learning Adaptive Multi-Objective Robot Navigation with Demonstrations

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dengler

deheuvel

kreis

dawood

Amani

Roshdi

Shokry

Embodied AI at LAMARR Institute for Machine Learning and Artificial Intelligence

Safe Multi-Agent Reinforcement Learning for Formation Control Without Individual Reference Targets

Handling Sparse Rewards in Reinforcement Learning Using Model Predictive Control

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

Viewpoint Push Planning for Mapping of Unknown Confined Spaces

Spatiotemporal Attention Enhances Lidar-Based Robot Navigation in Dynamic Environments

Integrating One-Shot View Planning with a Single Next-Best View via Long-Tail Multiview Sampling

Ensemble Policies for Diverse Query-Generation in Preference Alignment of Robot Navigation

Learning Adaptive Multi-Objective Robot Navigation with Demonstrations

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