Distributed generalization of learned planning models in robot programming by demonstration
Distributed generalization of learned planning models in robot programming by demonstration
复制标题
通过演示对机器人编程中的学习规划模型进行分布式泛化
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
R. Dillmann
中科院分区：
文献类型：
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作者：
Rainer Jäkel;Pascal Meissner;Sven R. Schmidt;R. Dillmann
在演示编程（PbD）中，自主学习的关键问题之一是自动提取操作任务的相关特征，这对泛化能力有重要影响。在本文中，任务特征被编码为学习规划模型的约束。为了提取相关的约束，人类老师演示了一组测试，例如一个有不同物体的场景，机器人尝试使用约束运动规划在每个测试上执行规划模型。基于在规划过程中哪些约束失效的统计数据，使用进化算法并行地改进了关于约束的最大子集的多个假设，这些假设允许在所有测试中找到解决方案。该算法在7个实验和2个机器人系统上进行了测试。
In Programming by Demonstration (PbD), one of the key problems for autonomous learning is to automatically extract the relevant features of a manipulation task, which has a significant impact on the generalization capabilities. In this paper, task features are encoded as constraints of a learned planning model. In order to extract the relevant constraints, the human teacher demonstrates a set of tests, e.g. a scene with different objects, and the robot tries to execute the planning model on each test using constrained motion planning. Based on statistics about which constraints failed during the planning process multiple hypotheses about a maximal subset of constraints, which allows to find a solution in all tests, are refined in parallel using an evolutionary algorithm. The algorithm was tested on 7 experiments and two robot systems.