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

Abstract

a) (b) (c) (d) Fig. 1: Our five-fingered dexterous hand solves a scrambled Rubik's Cube by operating its layers and changing its pose.Our method starts with a random state (a), plans the optimal move sequence (b,c), and reaches the desired state (d).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%.

A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods

“…It is obvious that different methods could be flexibly combined to achieve better performance due to various task scenarios. For example, Li et al ( 2019b ) propose a hierarchical deep RL method for planning and manipulation separately. For rubik's cube playing task, the model based Iterative Deepening A* (IDA*) search algorithm is adopted to find the optimal move sequence, and the model free Hindsight Experience Replay (HER) method is taken as the operator to learn from sparse rewards.…”

Dexterous Manipulation for Multi-Fingered Robotic Hands With Reinforcement Learning: A Review

“…The sources of demonstrations can be kinesthetic teaching, teleoperation (Zahlner S. et al, (n.d.); Handa et al, 2019 ; Li T. et al, 2019 ; Li et al, 2020 ), raw video, and so on. The problem of learning from demonstration has been studied a lot in recent years and a comprehensive survey can be seen in Ramírez et al ( 2021 ).…”