59 citations · 219 across the 16 of their papers we have counts for
4 papers · 1 filter
Safe-To-Explore State Spaces: Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization
Jens Lundell, Robert Krug, Erik Schaffernicht +2
Policy search reinforcement learning allows robots to acquire skills by themselves. However, the learning procedure is inherently unsafe as the robot has no a-priori way to predict…
Deep Network Uncertainty Maps for Indoor Navigation
Francesco Verdoja, Jens Lundell, Ville Kyrki
Most mobile robots for indoor use rely on 2D laser scanners for localization, mapping and navigation. These sensors, however, cannot detect transparent surfaces or measure the full…
Imitating Human Search Strategies for Assembly
Dennis Ehlers, Markku Suomalainen, Jens Lundell +1
We present a Learning from Demonstration method for teaching robots to perform search strategies imitated from humans in scenarios where alignment tasks fail due to position uncert…
Hallucinating robots: Inferring Obstacle Distances from Partial Laser Measurements
Jens Lundell, Francesco Verdoja, Ville Kyrki
Many mobile robots rely on 2D laser scanners for localization, mapping, and navigation. However, those sensors are unable to correctly provide distance to obstacles such as glass p…