Interleaving Monte Carlo Tree Search and Self-Supervised Learning for Object Retrieval in Clutter

TL;DR

This paper introduces MORE, a self-supervised framework combining Monte Carlo Tree Search and deep learning to improve object retrieval in cluttered scenes, achieving efficiency and solution quality improvements.

Contribution

The paper presents a novel learning-guided MCTS framework that integrates deep neural networks for efficient and effective object retrieval in cluttered environments.

Findings

MORE significantly reduces computation time.

The framework improves retrieval success rates.

It demonstrates effective real-world robotic application.

Abstract

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