2.3k citations · 4.7k across the 84 of their papers we have counts for
3 papers · 1 filter
Acquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning
Wenbin Li, Jeannette Bohg, Mario Fritz
Understanding physical phenomena is a key component of human intelligence and enables physical interaction with previously unseen environments. In this paper, we study how an artif…
Combining Learned and Analytical Models for Predicting Action Effects from Sensory Data
Alina Kloss, Stefan Schaal, Jeannette Bohg
One of the most basic skills a robot should possess is predicting the effect of physical interactions with objects in the environment. This enables optimal action selection to reac…
Real-time Perception meets Reactive Motion Generation
Daniel Kappler, Franziska Meier, Jan Issac +7
We address the challenging problem of robotic grasping and manipulation in the presence of uncertainty. This uncertainty is due to noisy sensing, inaccurate models and hard-to-pred…