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.

Abstract
 1. 서 론
 2. 관련 연구
 3. 심층 시각기반 행동 정책의 제한된 탐색
  3.1 GPS
  3.2 GPS with BADMM
  3.3 시각기반 딥러닝 정책학습
 4. GPS를 백스터 연구 로봇에 적용하는 과정
  4.1 Guided policy search 코드의 구조
  4.2 백스터 에이전트 클래스의 설계
 5. 실 험
  5.1 블록 조립 태스크
  5.2 블록 파지 태스크
 6. 결론 및 추후과제
 References

• 김성운(UNIST) | Seongun Kim
• 김솔아(UNIST) | Sol A Kim
• 하파엘 리마(UNIST) | Rafael de Lima
• 최재식(Computer Engineering, UNIST) | Jaesik Choi Corresponding author