TAILIEUCHUNG - Robot Manipulator_2

Robots can be considered as the most advanced automatic systems and robotics, as a technique and scientific discipline, can be considered as the evolution of automation with interdisciplinary integration with other technological fields. A robot can be defined as a system which is able to perform several manipulative tasks with objects, tools, and even its extremity (end-effector) with the capability of being reprogrammed for several types of operations. | 14 Novel Framework of Robot Force Control Using Reinforcement Learning Byungchan Kim1 and Shinsuk Park2 Center for Cognitive Robotics Research Korea Institute of Science and Technology 2Department of Mechanical Engineering Korea University Korea 1. Introduction Over the past decades robotic technologies have advanced remarkably and have been proven to be successful especially in the field of manufacturing. In manufacturing conventional position-controlled robots perform simple repeated tasks in static environments. In recent years there are increasing needs for robot systems in many areas that involve physical contacts with human-populated environments. Conventional robotic systems however have been ineffective in contact tasks. Contrary to robots humans cope with the problems with dynamic environments by the aid of excellent adaptation and learning ability. In this sense robot force control strategy inspired by human motor control would be a promising approach. There have been several studies on biologically-inspired motor learning. Cohen et al. suggested impedance learning strategy in a contact task by using associative search network Cohen et al. 1991 . They applied this approach to wall-following task. Another study on motor learning investigated a motor learning method for a musculoskeletal arm model in free space motion using reinforcement learning Izawa et al. 2002 . These studies however are limited to rather simple problems. In other studies artificial neural network models were used for impedance learning in contact tasks Jung et al. 2001 Tsuji et al. 2004 . One of the noticeable works by Tsuji et al. suggested on-line virtual impedance learning method by exploiting visual information. Despite of its usefulness however neural network-based learning involves heavy computational load and may lead to local optimum solutions easily. The purpose of this study is to present a novel framework of force control for robotic contact tasks. To develop appropriate .

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