100 citations · 100 across the 1 of their papers we have counts for
3 papers
Learning visual servo policies via planner cloning
Ulrich Viereck, Kate Saenko, Robert Platt
Learning control policies for visual servoing in novel environments is an important problem. However, standard model-free policy learning methods are slow. This paper explores plan…
Adapting control policies from simulation to reality using a pairwise loss
Ulrich Viereck, Xingchao Peng, Kate Saenko +1
This paper proposes an approach to domain transfer based on a pairwise loss function that helps transfer control policies learned in simulation onto a real robot. We explore the id…
Learning a visuomotor controller for real world robotic grasping using simulated depth images
Ulrich Viereck, Andreas ten Pas, Kate Saenko +1
We want to build robots that are useful in unstructured real world applications, such as doing work in the household. Grasping in particular is an important skill in this domain, y…