A Computational Framework for Robot Hand Design via Reinforcement Learning

Abstract

Robot hand is essential for a fully functional robot and designing a good robot hand is a sophisticated job that challenges the designer’s knowledge and experience. This paper presents a computational framework for automatic optimal robot hand design based on reinforcement learning (RL), which considers desired grasping tasks, grasp control strategies,and performance quality measures altogether. The RL-based framework intends to grow finger joints with different types and link lengths at different positions from null. Then, the reward function for such a growing action is defined in terms of quality indexes of the generated robot hand to perform desired grasping tasks under expected control strategies. To demonstrate the effectiveness of this framework, in this paper we set the desired task to simply grasping objects of three primitive shapes (i.e., box, cylinder, and sphere) with predefined hand positions and strategies to close fingers to achieve grasps for each object. The force closure condition, quantitative stability indexes, and energy consumption of grasps as well as some penalty terms are used to assemble the reward function. Through simulation and practical prototype experiments, we show that capable robot hands can be automatically generated by the proposed framework. Potential factors that affect the output of the framework and deserve further exploration are also discussed.