Robot co-design via bi-level optimization couples within-lifetime controller learning for fitness evaluation with cross-generational morphological evolution. Prior work has established that well-adapted morphology facilitates faster control learning, a property termed morphological intelligence. Yet how control learnin...
Jun-Ru Song, Yang Yang, Yaqing Xu et al.· 0 citations
MISCO is developed, a novel evolutionary framework empowered by deep generative models to optimize VSR designs with theoretical guarantees that represents a step change towards more scalable and reliable soft robot development.
Jun-Ru Song, Huan Xiao, Yang Yang et al.· 0 citations
This work presents RoboLDA, a Bayesian probabilistic model that decomposes VSR morphology generation into a four-level hierarchy:"task-robot-organ-voxel", and is trained via variational inference, which pioneers hierarchical generative modeling of robot morphology.
Jun-Ru Song, Yang Yang, Jing-Dan Shi et al.· 0 citations
Topology optimization provides innovative solutions for lightweight structural design by rationally arranging material distribution. It enhances structural performance while reducing material consumption and structural weight, thereby significantly lowering production and operational costs and generating enormous econ...
Yu-Ting Tang, Yu Li, Jiaxiang Luo et al.· SAE technical paper series· 0 citations
CoNav-UAV is proposed, which explicitly models the target-oriented vision-and-language navigation task as a Stackelberg game between a high-altitude leader and a low-altitude follower, with the system operating on onboard visual and linguistic inputs alone.
Jun-Ru Song, Wenhao Zhang, Yang Yang et al.· 2 citations
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