Accelerating the Construction of Boundaries of Feasibility in Three Classes of Robot Design Problems
Accelerating the Construction of Boundaries of Feasibility in Three Classes of Robot Design Problems
复制标题
加速构建三类机器人设计问题的可行性边界
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
J. O’Kane
中科院分区：
文献类型：
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作者：
S. Ghasemlou;J. O’Kane
本文旨在提高自动化工具的实用可扩展性，以协助设计机器人。这些问题很快变得棘手，因为潜在的设计空间是巨大的。我们考虑一种特定类型的设计工具，在以前的工作中，它构造了一个代表性的破坏性边界的机器人设计的空间。这项先前的工作表明，该边界的清晰表示，特别是决策树，可以说明传感或驱动系统的哪些元素对于使机器人完成其任务最重要。在这种情况下，机器人与世界的交互被表示为procrustean图，机器人设计的空间由重写该图上的标签的标签映射的空间表示。在本文中，我们扩展了这些结果，展示了领域知识如何使这些工具能够在合理的时间范围内找到更复杂问题的解决方案。具体来说，我们提出了三种不同的情况下，表示为p-图和标签地图上的约束，在这种情况下，识别破坏性边界的学习算法可以快速收敛到高精度的结果，在更大的规模比以前的通用算法的问题。每一种情况的条件都很容易验证，每一种情况下的问题集都足够丰富，可以包含几个有趣的问题。实验结果证明了该方法的有效性。
This paper aims to improve the practical scalability of automated tools to assist in designing robots. Such problems rapidly become intractable because the underlying design space is immense. We consider a specific type of design tool addressed in prior work, which constructs a representation of the destructiveness boundary in the space of robot designs. This prior work showed that a legible representation, specifically a decision tree, of this boundary can illuminate which elements of a sensing or actuation system are most important for enabling the robot to complete its task. In that context, the robot’s interaction with the world is represented as procrustean graph, and the space of robot designs is represented by the space of label maps that rewrite the labels on that graph. In this paper, we expand upon those results by showing how domain knowledge can enable such tools to find solutions to more complex problems within a reasonable time frame. Specifically, we propose three different scenarios, expressed as constraints on the p-graph and on the label maps, under which the learning algorithm to identify the destructiveness boundary can converge quickly to high accuracy results for problems at larger scales than the prior, general-purpose algorithm. The conditions for each of these scenarios are easily verifiable and the set of problems that fall under each is rich enough to encompass several interesting problems. Experimental results demonstrate the effectiveness of the proposed methods.