  In this study, working with the task of object retrieval in clutter, we have
developed a robot learning framework in which Monte Carlo Tree Search (MCTS) is
first applied to enable a Deep Neural Network (DNN) to learn the intricate
interactions between a robot arm and a complex scene containing many objects,
allowing the DNN to partially clone the behavior of MCTS. In turn, the trained
DNN is integrated into MCTS to help guide its search effort. We call this
approach learning-guided Monte Carlo tree search for Object REtrieval (MORE),
which delivers significant computational efficiency gains and added solution
optimality. MORE is a self-supervised robotics framework/pipeline capable of
working in the real world that successfully embodies the System 2 to System 1
learning philosophy proposed by Kahneman, where learned knowledge, used
properly, can help greatly speed up a time-consuming decision process over
time. Videos and supplementary material can be found at
https://github.com/arc-l/more
