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
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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