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학술지 Reinforcement Learning-based Path Generation using Sequential Pattern Reduction and Self-directed Curriculum Learning
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김태우, 이주행
IEEE Access, v.8, pp.147790-147807
20HS2500, 고령 사회에 대응하기 위한 실환경 휴먼케어 로봇 기술 개발, 이재연
Recent advancements in robots and deep learning have led to active research in human-robot interaction. However, non-physical interaction using visual devices such as laser pointers has gained less attention than physical interaction using complex robots such as humanoids. Such vision-based interaction has high potential for use in recent human-robot collaboration environments such as assembly guidance, even with a minimum amount of configuration. In this paper, we introduce a simple robotic laser pointer device that follows an arbitrary planar path and is designed to be a visual instructional aid. We also propose an image-based automatic path generation method using reinforcement learning and a sequential pattern reduction technique. However, such vision-based human-robot interaction is generally performed in a dynamic environment, and it can frequently be necessary to calibrate the devices more than once. In this paper, we avoid the need for this re-calibration process through episodic randomization learning and improved learning efficiency. In particular, contrary to previous approaches, the agent controls the curriculum difficulty in a self-directed manner to determine the optimal curriculum. To our knowledge, this is the first study of curriculum learning that incorporates an explicit learning environment control signal initiated by the agent itself. Through quantitative and qualitative analyses, we show that the proposed self-directed curriculum learning method outperforms ordinary episodic randomization and curriculum learning. We hope that the proposed method can be extended to a general reinforcement learning framework.
KSP 제안 키워드
Control Signal, Curriculum learning, Dynamic Environment, Environment Control, High potential, Human-Robot Interaction(HRI), Image-based, Learning Environment, Learning efficiency, Learning framework, Learning methods
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