Article

GES: Accelerating Morphology-Control Co-Design for Buoyancy-Assisted Robots with Gaussian Evolutionary Specialists

This study proposes Gaussian Evolutionary Specialists (GES), a reinforcement learning-based co-design framework. By maintaining a population of specialist policies whose design-space territories evolve according to their control performance, it mitigates behavioral diversity collapse, in which a single shared policy converges to the same control strategy across different morphologies.

Part of the event: GES Framework Co-Design Optimizes BALLU Robot Morphology and Control

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