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arXiv · 2012.04195

Neural fidelity warping for efficient robot morphology design

Sha Hu·Zeshi Yang·Greg Mori
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Abstract

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.

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Sha Hu, Zeshi Yang, Greg Mori. 2020-12-08. Neural fidelity warping for efficient robot morphology design. https://arxiv.org/abs/2012.04195

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