Multi-Objective Graph Heuristic Search for Terrestrial Robot Design
Multi-Objective Graph Heuristic Search for Terrestrial Robot Design
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DOI：
10.1109/icra48506.2021.9561818
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Jie Xu;A. Spielberg;Allan Zhao;D. Rus;W. Matusik
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文献类型：
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作者：
Jie Xu;A. Spielberg;Allan Zhao;D. Rus;W. Matusik
我们提出的方法，共同设计刚性机器人的控制和形态（包括离散拓扑结构）在多个目标。以前的工作已经解决了单目标机器人协同设计或多目标控制的问题。然而，联合多目标协同设计问题是非常重要的生成能力，多功能，算法设计的机器人。在这项工作中，我们提出了多目标图启发式搜索，它扩展了单目标图启发式搜索从以前的工作，使一个高效的多目标搜索在组合设计拓扑空间。这种方法的核心，我们引入了一个新的通用的，多目标的启发式函数的基础上图神经网络，能够有效地利用不同的任务之间的权衡学习信息。我们展示了我们的方法，七个地面运动和设计任务的六个组合，包括一个三目标的例子。我们比较了不同方法捕获的Pareto前沿，并证明了我们的多目标图启发式搜索在定量和定性上优于其他技术。
We present methods for co-designing rigid robots over control and morphology (including discrete topology) over multiple objectives. Previous work has addressed problems in single-objective robot co-design or multi-objective control. However, the joint multi-objective co-design problem is extremely important for generating capable, versatile, algorithmically designed robots. In this work, we present Multi-Objective Graph Heuristic Search, which extends a single-objective graph heuristic search from previous work to enable a highly efficient multi-objective search in a combinatorial design topology space. Core to this approach, we introduce a new universal, multiobjective heuristic function based on graph neural networks that is able to effectively leverage learned information between different task trade-offs. We demonstrate our approach on six combinations of seven terrestrial locomotion and design tasks, including one three-objective example. We compare the captured Pareto fronts across different methods and demonstrate that our multi-objective graph heuristic search quantitatively and qualitatively outperforms other techniques.