Learning to bag with a simulation‐free reinforcement learning framework for robots  

作　　者：Francisco Munguia-Galeano Jihong Zhu Juan David Hernández Ze Ji 

机构地区：[1]Cooper Group,University of Liverpool,Liverpool,UK [2]School of Physics Engineering and Technology,University of York,York,UK [3]School of Computer Science and Informatics,Cardiff University,Cardiff,UK [4]School of Engineering,Cardiff University,Cardiff,UK

出　　处：《IET Cyber-Systems and Robotics》2024年第2期52-66,共15页智能系统与机器人（英文）

基　　金：This work was partially supported by Consejo Nacional de Humanidades,Ciencias y Tecnologías(CONAHCyT);the Engineering and Physical Sciences Research Council(grant No.EP/X018962/1).

摘　　要：Bagging is an essential skill that humans perform in their daily activities.However,deformable objects,such as bags,are complex for robots to manipulate.A learning-based framework that enables robots to learn bagging is presented.The novelty of this framework is its ability to learn and perform bagging without relying on simulations.The learning process is accomplished through a reinforcement learning(RL)algorithm introduced and designed to find the best grasping points of the bag based on a set of compact state representations.The framework utilises a set of primitive actions and represents the task in five states.In our experiments,the framework reached 60% and 80% success rates after around 3 h of training in the real world when starting the bagging task from folded and unfolded states,respectively.Finally,the authors test the trained RL model with eight more bags of different sizes to evaluate its generalisability.

关 键 词：reinforcement learning robot learning ROBOTICS 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]