基于经验指导的认知机器人鲁棒任务执行

Sanem Sariel, Petek Yildiz, Sertac Karapinar, Dogan Altan, Melis Kapotoglu
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引用次数: 5

摘要

任务执行的稳健性需要持续计划、监控、推理和学习过程的紧密集成。在本文中,我们研究了如何通过从经验中学习来确保鲁棒性。采用归纳逻辑规划(ILP)作为学习方法,对故障情况进行假设。它提供了机器人经验的一阶逻辑表示。机器人利用这种经验构建启发式来指导它未来的决策。在先锋3-AT机器人上分析了学习引导规划过程的性能。结果表明,针对故障情况所建立的假设是合理的,保证了机器人在未来任务中的安全性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust task execution through experience-based guidance for cognitive robots
Robustness in task execution requires tight integration of continual planning, monitoring, reasoning and learning processes. In this paper, we investigate how robustness can be ensured by learning from experience. Our approach is based on a learning guided planning process for a robot that gains its experience from action execution failures through lifelong experiential learning. Inductive Logic Programming (ILP) is used as the learning method to frame hypotheses for failure situations. It provides first-order logic representation of the robot's experience. The robot uses this experience to construct heuristics to guide its future decisions. The performance of the learning guided planning process is analyzed on our Pioneer 3-AT robot. The results reveal that the hypotheses framed for failure cases are sound and ensure safety and robustness in future tasks of the robot.
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