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arXiv AI By Junru Song, Yang Yang, Jingdan Shi, Guozhen Li, Weien Zhou, Ying Wen, Feifei Wang, Wen Yao, Tingsong Jiang

RoboLDA: A Probabilistic Generative Model for Uncovering Embodied Hierarchical Structures in Voxel-based Soft Robots

Read the original on arXiv AI →

RoboLDA is a Bayesian probabilistic model that learns a four‑level hierarchy—task, robot, organ, voxel—from existing high‑performing voxel‑based soft robot designs. By training with variational inference, it uncovers consistent, intuitive hierarchical patterns and can generate new robot morphologies that outperform evolutionary algorithms by an average of 106.4% without further optimization. The inferred organ structures also improve modular control policies, demonstrating the model’s utility for zero‑shot design and motion control.

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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