Learning to Solve a Rubik’s Cube with a Dexterous Hand

By Tingguang Li, Weitao Xi, Meng Fang, Jia Xu, Max Qing-Hu Meng. 2019

15 citations recorded by the source corpus.

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%. To benefit the community, we make our simulator and source code publicly available to advance future research1.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

Solving Rubik's Cube with a Robot Hand (2019, background): “While many robots capable of solving a Rubik’s Cube exist today [8, 34, 78, 44], all of them which we are aware of have been built exclusively for this purpose and cannot generalize to other manipulation tasks.”