생물학적인 셀 인젝션 기술은 유전자 주입, 시험관 배양, 인공수정 및 신약개발 분야에서 광범위하게 사용되어오고 있는 기술이다. 생물공학에서 다루는 셀 인젝션 기술은 크게 착생세포 인젝션과 서스펜디드 셀 인젝션으로 구분할 수 있다. 최근 상용화 장비로 출시되고 있는 것들은 착생세포에 대한 자동 인젝션 시스템이 대부분을 차지하고 있다. 반면, 서스펜디드 셀 인젝션 시스템의 경우는 비교적 최근들어 자동화 장비 및 방법론의 개발에 대한 논의가 이루어지고 있는

Reinforcement learning has been applied to various problems in robotics. However, it was still hard to train complex robotic manipulation tasks since there is a few models which can be applicable to general tasks. Such general models require a lot of training episodes. In these reasons, deep neural networks which have shown to be good function approximators have not been actively used for robot manipulation task. Recently, some of these challenges are solved by a set of methods, such as Guided Policy Search, which guide or limit search directions while training of a deep neural network based policy model. These frameworks are already applied to a humanoid robot, PR2. However, in robotics, it is not trivial to adjust existing algorithms designed for one robot to another robot. In this paper, we present our implementation of Guided Policy Search to the robotic arms of the Baxter Research Robot. To meet the goals and needs of the project, we build on an existing implementation of Baxter Agent class for the Guided Policy Search algorithm code using the built-in Python interface. This work is expected to play an important role in popularizing robot manipulation reinforcement learning methods on cost-effective robot platforms.