1 citations · 1 across the 3 of their papers we have counts for
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ECo-MoE: Embodiment-Conditioned Mixture of Experts Increases the Evolvability of Robots
Yibin Wang, Muhan Li, Zihan Guo +1
In this paper, we introduce a model of evolution and learning in robots that co-optimizes a distribution of latent design vectors (genotypes) and a mixture of control experts (neur…
Creating manufacturable blueprints for coarse-grained virtual robots
Zihan Guo, Muhan Li, Shuzhe Zhang +1
Over the past three decades, countless embodied yet virtual agents have freely evolved inside computer simulations, but vanishingly few were realized as physical robots. This is be…
Generating Freeform Endoskeletal Robots
Muhan Li, Lingji Kong, Sam Kriegman
The automatic design of embodied agents (e.g. robots) has existed for 31 years and is experiencing a renaissance of interest in the literature. To date however, the field has remai…
Reinforcement learning for freeform robot design
Muhan Li, David Matthews, Sam Kriegman
Inspired by the necessity of morphological adaptation in animals, a growing body of work has attempted to expand robot training to encompass physical aspects of a robot's design. H…