Learning to Solve a Rubik's Cube with a Dexterous Hand
arXiv:1907.11388
The paper introduces a hierarchical deep reinforcement learning system that combines a model‑based planner and a model‑free controller to enable a 24‑DoF dexterous robot hand to solve randomly scrambled Rubik's cubes, achieving about 90% success in simulation.
Abstract
We present a learning-based approach to solving a Rubik's cube with a multi-fingered dexterous hand. Despite the promising performance of dexterous in-hand manipulation, solving complex tasks which involve multiple steps and diverse internal object structure has remained an important, yet challenging task. In this paper, we tackle this challenge with a hierarchical deep reinforcement learning method, which separates planning and manipulation. A model-based cube solver finds an optimal move sequence for restoring the cube and a model-free cube operator controls all five fingers to execute each move step by step. To train our models, we build a high-fidelity simulator which manipulates a Rubik's Cube, an object containing high-dimensional state space, with a 24-DoF robot hand. Extensive experiments on 1400 randomly scrambled Rubik's cubes demonstrate the effectiveness of our method, achieving an average success rate of 90.3%.
7 pages, 6 figures