Matthew Wilson

Learning to Manipulate Object Collections Using Grounded State Representations

We propose a method for sim-to-real robot learning which exploits simulator state information in a way that scales to many objects. First, we train a pair of encoders on raw object pose targets to learn representations that accurately capture the state information of a multi-object environment. Seco