21 citations · 31 across the 8 of their papers we have counts for
8 papers
Hey Robot! Personalizing Robot Navigation through Model Predictive Control with a Large Language Model
Diego Martinez-Baselga, Oscar de Groot, Luzia Knoedler +3
Robot navigation methods allow mobile robots to operate in applications such as warehouses or hospitals. While the environment in which the robot operates imposes requirements on i…
DWA-3D: A Reactive Planner for Robust and Efficient Autonomous UAV Navigation in Confined Environments
Jorge Bes, Juan Dendarieta, Luis Riazuelo +1
Despite the growing impact of Unmanned Aerial Vehicles (UAVs) across various industries, most of current available solutions lack for a robust autonomous navigation system to deal…
AVOCADO: Adaptive Optimal Collision Avoidance driven by Opinion
Diego Martinez-Baselga, Eduardo Sebastián, Eduardo Montijano +3
We present AVOCADO (AdaptiVe Optimal Collision Avoidance Driven by Opinion), a novel navigation approach to address holonomic robot collision avoidance when the robot does not know…
SHINE: Social Homology Identification for Navigation in Crowded Environments
Diego Martinez-Baselga, Oscar de Groot, Luzia Knoedler +3
Navigating mobile robots in social environments remains a challenging task due to the intricacies of human-robot interactions. Most of the motion planners designed for crowded and…
RUMOR: Reinforcement learning for Understanding a Model of the Real World for Navigation in Dynamic Environments
Diego Martinez-Baselga, Luis Riazuelo, Luis Montano
Autonomous navigation in dynamic environments is a complex but essential task for autonomous robots, with recent deep reinforcement learning approaches showing promising results. H…
Improving robot navigation in crowded environments using intrinsic rewards
Diego Martinez-Baselga, Luis Riazuelo, Luis Montano
Autonomous navigation in crowded environments is an open problem with many applications, essential for the coexistence of robots and humans in the smart cities of the future. In re…
Deep reinforcement learning oriented for real world dynamic scenarios
Diego Martinez, Luis Riazuelo, Luis Montano
Autonomous navigation in dynamic environments is a complex but essential task for autonomous robots. Recent deep reinforcement learning approaches show promising results to solve t…
3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation
Iñigo Alonso, Luis Riazuelo, Luis Montesano +1
LIDAR semantic segmentation, which assigns a semantic label to each 3D point measured by the LIDAR, is becoming an essential task for many robotic applications such as autonomous d…