Learning to Grasp with Primitive Shaped Object Policies

摘要

Towards the automation of assembly tasks using industrial robot manipulators, improving the robotic grasping is essential. In this paper, we employed a reinforcement learning method based on the policy search algorithm, call Guided Policy Search, to learn policies for the grasping problem. The goal was to evaluate if policies trained solely using sets of primitive shaped objects, can still achieve the task of grasping objects of more complex shapes. The results show that even using simple shaped objects; the method can learn policies that generalize to more complex shapes. Additionally, a robustness test was conducted to show that the visual component of the policy helps to guide the system when there is an error in the estimation of the target object pose.