{"title":"一种基于任务序列增量学习的响应式导航方法","authors":"F. Davesne, C. Barret","doi":"10.1109/ROMOCO.1999.791048","DOIUrl":null,"url":null,"abstract":"Within the contest of learning sequences of basic tasks to build a complex behavior, a method is proposed to coordinate a hierarchical set of tasks. Each one possesses a set of sub-tasks lower in the hierarchy, which must be coordinated to respect a binary perceptive constraint. For each task, the coordination is achieved by a reinforcement learning inspired algorithm based on the heuristic which does not need internal parameters. A validation of the method is given, using a simulated Khepera robot. A goal-seeking behavior is divided into three tasks: go to the goal, follow a wall on the left and on the right. The last two tasks utilize basic behaviors and two other sub-tasks: avoid obstacles on the left and on the right. All the tasks may use a set of 5 basic behaviors. The global goal-seeking behavior and the wall-following and the obstacle avoidance tasks are learned during a step by step learning process.","PeriodicalId":131049,"journal":{"name":"Proceedings of the First Workshop on Robot Motion and Control. RoMoCo'99 (Cat. No.99EX353)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A reactive navigation method based on an incremental learning of tasks sequences\",\"authors\":\"F. Davesne, C. Barret\",\"doi\":\"10.1109/ROMOCO.1999.791048\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Within the contest of learning sequences of basic tasks to build a complex behavior, a method is proposed to coordinate a hierarchical set of tasks. Each one possesses a set of sub-tasks lower in the hierarchy, which must be coordinated to respect a binary perceptive constraint. For each task, the coordination is achieved by a reinforcement learning inspired algorithm based on the heuristic which does not need internal parameters. A validation of the method is given, using a simulated Khepera robot. A goal-seeking behavior is divided into three tasks: go to the goal, follow a wall on the left and on the right. The last two tasks utilize basic behaviors and two other sub-tasks: avoid obstacles on the left and on the right. All the tasks may use a set of 5 basic behaviors. The global goal-seeking behavior and the wall-following and the obstacle avoidance tasks are learned during a step by step learning process.\",\"PeriodicalId\":131049,\"journal\":{\"name\":\"Proceedings of the First Workshop on Robot Motion and Control. RoMoCo'99 (Cat. No.99EX353)\",\"volume\":\"108 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the First Workshop on Robot Motion and Control. RoMoCo'99 (Cat. No.99EX353)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ROMOCO.1999.791048\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the First Workshop on Robot Motion and Control. RoMoCo'99 (Cat. No.99EX353)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROMOCO.1999.791048","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A reactive navigation method based on an incremental learning of tasks sequences
Within the contest of learning sequences of basic tasks to build a complex behavior, a method is proposed to coordinate a hierarchical set of tasks. Each one possesses a set of sub-tasks lower in the hierarchy, which must be coordinated to respect a binary perceptive constraint. For each task, the coordination is achieved by a reinforcement learning inspired algorithm based on the heuristic which does not need internal parameters. A validation of the method is given, using a simulated Khepera robot. A goal-seeking behavior is divided into three tasks: go to the goal, follow a wall on the left and on the right. The last two tasks utilize basic behaviors and two other sub-tasks: avoid obstacles on the left and on the right. All the tasks may use a set of 5 basic behaviors. The global goal-seeking behavior and the wall-following and the obstacle avoidance tasks are learned during a step by step learning process.