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%.

Links

Paper: available on arXiv

Code: available on github

Contact: tgli {at} link {dot} cuhk {dot} edu {dot} hk for more information

Bibtex

@article{li2019learning,

  title={Learning to Solve a Rubik's Cube with a Dexterous Hand},

  author={Li, Tingguang and Xi, Weitao and Fang, Meng and Xu, Jia and Meng, Max Qing-Hu},

  journal={arXiv preprint arXiv:1907.11388},

  year={2019}

}