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arXiv AI By Junru Song, Huan Xiao, Yang Yang, Guozhen Li, Wei Peng, Xiaoya Zhang, Tingsong Jiang, Weien Zhou, Ying Wen, Feifei Wang, Wen Yao

Generative Evolutionary Design of Voxel-Based Soft Robots with Provable Optimality

Read the original on arXiv AI →

The paper introduces MISCO, an evolutionary framework that uses deep generative models to design voxel-based soft robots (VSRs). MISCO combines an estimation-of-distribution algorithm with a variational autoencoder that includes multi-task learning, position awareness, and inter-voxel signaling to improve representation and sampling efficiency. The authors provide theoretical guarantees of asymptotic convergence to globally optimal designs and demonstrate through simulations that MISCO effectively navigates large design spaces, producing high-performing VSRs for various tasks while balancing efficiency and diversity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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