碩士 / 國立臺灣大學 / 電機工程學研究所 / 105 / For service robot, the navigation movement that only considers the metrics such as minimum path is not enough. In the environment that robot and human coexist, the robot not only needs to consider such metric but also to let the human think its navigation movement is natural enough. In order to following such ’social norms’ in the environment, using learning method to make robot learn how to navigate is easier than tediously designing handcrafted rules. Recently, deep reinforcement learning (DRL) is applied to the robotic field. However, there are very few researchers who consider solving the social navigation problem, which is in a high dimensional space by applying DRL method. In order to solve these problems, the research proposes the composite reinforcement learning (CRL) system that provide a framework that use the sensor input to learn how to generate the velocity of the robot. The system uses DRL to learn the velocity in a given set of scenarios and a reward update module that provides ways of updating the reward function based on the feedback of human. In order to generalize the system, we don’t use simulator or pre-collected data that are in lack of the real interaction between human and robot. We directly apply our system to the real environment and provide methods to cope with the long training time problem of DRL in real environment by incorporating prior knowledge to the system. The CRL system is able to incrementally learn to determine its velocity by a given rules (e.g. reward functions). Also it will keep collecting human feedback to keep synchronizing the reward functions inside the system to the current social norms. The experiments show that the proposed CRL system can learn how to navigate in reasonable time. The updating reward is able to make the system learn a more suitable navigation style.
| Identifer | oai:union.ndltd.org:TW/105NTU05442041 |
| Date | January 2017 |
| Creators | Pei-Huai Ciou, 邱沛淮 |
| Contributors | 傅立成 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | en_US |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 78 |
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