Please use this identifier to cite or link to this item:
|Title:||HILPS : Human-in-Loop Policy Search for mobile robot navigation||Authors:||Wen, Mingxing
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2020||Source:||Wen, M., Yue, Y., Wu, Z., Mihankhan, E., & Wang, D. (2020). HILPS : Human-in-Loop Policy Search for mobile robot navigation. Proceedings of the International Conference on Control, Automation, Robotics and Vision (ICARCV). doi:10.1109/ICARCV50220.2020.9305366||metadata.dc.contributor.conference:||2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV)||Abstract:||Reinforcement learning has obtained increasing attention in mobile robot mapless navigation in recent years. However, there are still some obvious challenges including the sample efficiency, safety due to dilemma of exploration and exploitation. These problems are addressed in this paper by proposing the Human-in-Loop Policy Search (HILPS) framework, where learning from demonstration, learning from human intervention and Near Optimal Policy strategies are integrated together. Firstly, the former two make sure that expert experience grant mobile robot a more informative and correct decision for accomplishing the task and also maintaining the safety of the mobile robot due to the priority of human control. Then the Near Optimal Policy (NOP) provides a way to selectively store the similar experience with respect to the preexisting human demonstration, in which case the sample efficiency can be improved by eliminating exclusively exploratory behaviors. To verify the performance of the algorithm, the mobile robot navigation experiments are extensively conducted in simulation and real world. Results show that HILPS can improve sample efficiency and safety in comparison to state-of-art reinforcement learning.||URI:||https://hdl.handle.net/10356/146502||DOI:||10.1109/ICARCV50220.2020.9305366||Schools:||School of Electrical and Electronic Engineering||Research Centres:||ST Engineering-NTU Corporate Lab||Rights:||© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ICARCV50220.2020.9305366||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Conference Papers|
Updated on Sep 22, 2023
Updated on Sep 22, 2023
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.