论文标题
神经保真度扭曲有效机器人形态设计
Neural fidelity warping for efficient robot morphology design
论文作者
论文摘要
我们考虑在计算资源限制下优化机器人形态以实现目标任务的最佳性能的问题。每个形态设计的评估过程涉及学习设计控制器，该控制器可以消耗大量时间和计算资源。为了应对昂贵的机器人形态评估的挑战，我们提出了一个连续的多保真贝叶斯优化框架，该框架可以通过低保真评估有效利用计算资源。我们确定了非平稳性在忠诚空间上的问题。我们提出的忠实翘曲机制可以学习学习时期和任务的表示，以模拟连续的忠诚度评估之间的非平稳协方差，这证明对现成的固定式内核具有挑战性。各种实验表明，我们的方法可以利用低保真评估来有效搜索最佳的机器人形态，表现优于最先进的方法。
We consider the problem of optimizing a robot morphology to achieve the best performance for a target task, under computational resource limitations. The evaluation process for each morphological design involves learning a controller for the design, which can consume substantial time and computational resources. To address the challenge of expensive robot morphology evaluation, we present a continuous multi-fidelity Bayesian Optimization framework that efficiently utilizes computational resources via low-fidelity evaluations. We identify the problem of non-stationarity over fidelity space. Our proposed fidelity warping mechanism can learn representations of learning epochs and tasks to model non-stationary covariances between continuous fidelity evaluations which prove challenging for off-the-shelf stationary kernels. Various experiments demonstrate that our method can utilize the low-fidelity evaluations to efficiently search for the optimal robot morphology, outperforming state-of-the-art methods.
