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• W3205169585 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." @default.
• W3205169585 created "2021-10-25" @default.
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• W3205169585 date "2021-05-30" @default.
• W3205169585 modified "2023-10-06" @default.
• W3205169585 title "Neural fidelity warping for efficient robot morphology design" @default.
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• W3205169585 doi "https://doi.org/10.1109/icra48506.2021.9561733" @default.
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