18 citations · 21 across the 3 of their papers we have counts for
5 papers
Hierarchies of Planning and Reinforcement Learning for Robot Navigation
Jan Wöhlke, Felix Schmitt, Herke van Hoof
Solving robotic navigation tasks via reinforcement learning (RL) is challenging due to their sparse reward and long decision horizon nature. However, in many navigation tasks, high…
Social Navigation with Human Empowerment driven Deep Reinforcement Learning
Tessa van der Heiden, Florian Mirus, Herke van Hoof
Mobile robot navigation has seen extensive research in the last decades. The aspect of collaboration with robots and humans sharing workspaces will become increasingly important in…
Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks
Sanjay Thakur, Herke van Hoof, Juan Camilo Gamboa Higuera +2
Diversity of environments is a key challenge that causes learned robotic controllers to fail due to the discrepancies between the training and evaluation conditions. Training from…
Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement
Wouter Kool, Herke van Hoof, Max Welling
The well-known Gumbel-Max trick for sampling from a categorical distribution can be extended to sample elements without replacement. We show how to implicitly apply this 'Gumbe…
Attention, Learn to Solve Routing Problems!
Wouter Kool, Herke van Hoof, Max Welling
The recently presented idea to learn heuristics for combinatorial optimization problems is promising as it can save costly development. However, to push this idea towards practical…