63 citations · 203 across the 11 of their papers we have counts for
13 papers
Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning
Max Lodel, Bruno Brito, Álvaro Serra-Gómez +3
Search missions require motion planning and navigation methods for information gathering that continuously replan based on new observations of the robot's surroundings. Current met…
Inclined Quadrotor Landing using Deep Reinforcement Learning
Jacob E. Kooi, Robert Babuška
Landing a quadrotor on an inclined surface is a challenging maneuver. The final state of any inclined landing trajectory is not an equilibrium, which precludes the use of most conv…
DeepKoCo: Efficient latent planning with a task-relevant Koopman representation
Bas van der Heijden, Laura Ferranti, Jens Kober +1
This paper presents DeepKoCo, a novel model-based agent that learns a latent Koopman representation from images. This representation allows DeepKoCo to plan efficiently using linea…
Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning
Jonáš Kulhánek, Erik Derner, Robert Babuška
Visual navigation is essential for many applications in robotics, from manipulation, through mobile robotics to automated driving. Deep reinforcement learning (DRL) provides an ele…
Symbolic Regression Driven by Training Data and Prior Knowledge
J. Kubalík, E. Derner, R. Babuška
In symbolic regression, the search for analytic models is typically driven purely by the prediction error observed on the training data samples. However, when the data samples do n…
Symbolic Regression Methods for Reinforcement Learning
Jiří Kubalík, Erik Derner, Jan Žegklitz +1
Reinforcement learning algorithms can solve dynamic decision-making and optimal control problems. With continuous-valued state and input variables, reinforcement learning algorithm…