Delineating boundaries of feasibility between robot designs
Delineating boundaries of feasibility between robot designs
复制标题
描绘机器人设计之间可行性的界限
DOI：
10.1109/iros.2018.8593811
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Dylan A. Shell
中科院分区：
文献类型：
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作者：
S. Ghasemlou;J. O’Kane;Dylan A. Shell
出于对工具的需求，以帮助有效的机器人的设计，我们研究如何确定特定的传感器和执行器资源发挥的作用，使机器人实现有用的目的。而不是仅仅问“这个传感器足够吗？”我们基于使用这些集合完成给定任务的可行性对传感器和致动器集合的一般修改进行分类。我们的目标是探索之间的边界修改是破坏性的一个给定的规划问题，修改，不。由于这一边界本身可以是不切实际的大，经典的搜索方法是无益的，以总结该边界上的歧视性的功能。相反，我们提出了一个决策树学习方法，有效地构建一个紧凑的隐式表示的边界。这个想法是允许设计者使用先验知识来约束搜索，然后使用该工具来探测受这些约束的边界，深入了解机器人确保完成任务所需的信息。最终，我们设想一个互动的过程中，额外的限制反复包括新的光是棚。我们的目标是为交互式工具铺平道路，帮助机器人专家驾驭设计空间的复杂性。我们描述了这种方法的实施沿着的实验结果表明，该方法可以构建决策树的解释价值。我们的实验表明，一些领域知识（特别是挑选强调单调性的功能）大大提高了运行时间，只有微不足道的准确性降低。
Motivated by the need for tools to aid in the design of effective robots, we examine how to determine the role that particular sensing and actuator resources play in enabling a robot to achieve useful ends. Rather than merely asking “will this sensor suffice?” we classify general modifications to the set of sensors and actuators based on the feasibility of accomplishing given tasks using these sets. The goal is to probe the boundary between modifications that are destructive on a given planning problem, and modifications that are not. Since this boundary itself can be impractically large, classic search methods are of no avail to summarize discriminatory features on this boundary. Instead, we propose a decision tree learning method to efficiently construct a compact implicit representation of the boundary. The idea is to allow the designer to use prior knowledge to constrain the search, then use the tool to probe the boundary subject to those constraints, gaining insight into the information necessary for a robot to ensure task achievement. Ultimately we envision a interactive process where additional constraints are repeatedly included as new light is shed. We aim to pave the way for interactive tools that help the roboticist navigate the complexities of the design space. We describe an implementation of this approach along with experimental results that show that the method can construct decision trees with explanatory value. Our experiments suggest that some domain knowledge (specifically picking features that emphasize monotonicity) substantially improves running-time with only negligible reduction in accuracy.