3 papers
cs.RO2024
Scenario-Based Curriculum Generation for Multi-Agent Autonomous Driving
Axel Brunnbauer, Luigi Berducci, Peter Priller +2
The automated generation of diverse and complex training scenarios has been an important ingredient in many complex learning tasks. Especially in real-world application domains, su…
cs.RO2023
Enhancing Robot Learning through Learned Human-Attention Feature Maps
Daniel Scheuchenstuhl, Stefan Ulmer, Felix Resch +2
Robust and efficient learning remains a challenging problem in robotics, in particular with complex visual inputs. Inspired by human attention mechanism, with which we quickly proc…
cs.LG2021
Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing
Axel Brunnbauer, Luigi Berducci, Andreas Brandstätter +4
World models learn behaviors in a latent imagination space to enhance the sample-efficiency of deep reinforcement learning (RL) algorithms. While learning world models for high-dim…