- 期刊
- OpenAccess
Multi-Step Learning to Search for Dynamic Environment Navigation
並列摘要
While navigation could be done using existing rule-based approaches, it becomes more attractive to use learning from demonstration (LfD) approaches to ease the burden of tedious rule designing and parameter tuning procedures. In our previous work, navigation in simple dynamic environments is achieved using the Learning to Search (LEARCH) algorithm with a proper feature set and the proposed data set refinement procedure. In this paper, the multi-step learning approach with goal-related information is proposed to further capture the successive motion behavior of the user in complex environments. The behaviors of the demonstrator could be matched by the motion control module in which policies of the demonstrator are well captured.
被引用紀錄
Yu, C. C. (2016). 自示範中學習結合與行動相關聯之資料調整法生成適用於動態場景之反應式行動法則 [master's thesis, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU201610401
延伸閱讀
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