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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
Physically-Consistent Parameter Identification of Robots in Contact
A Dynamic Safety Shield for Safe and Efficient Reinforcement Learning of Navigation Tasks
Integrating One-Shot View Planning with a Single Next-Best View via Long-Tail Multiview Sampling
Context-Based Meta Reinforcement Learning for Robust and Adaptable Peg-in-Hole Assembly Tasks
Farrag
Kutsch
Learning Goal-Directed Object Pushing in Cluttered Scenes with Location-Based Attention
Map Space Belief Prediction for Manipulation-Enhanced Mapping
Compact Multi-Object Placement Using Adjacency-Aware Reinforcement Learning
maren
dengler
deheuvel
kreis
dawood
Amani
Roshdi
Shokry
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.