Morphology Evolution for Embodied Robot Design With a Classifier-Guided Diffusion Model

Automatic design of intelligent robots plays a central role and has been a trending topic in embodied intelligence. A promising approach is the co-design framework, wherein evolutionary algorithms (EAs) are employed to optimize the robot’s morphology while reinforcement learning algorithms are utilized to refine its control strategies. However, the discrete morphology design space introduces significant challenges for EAs, to efficiently identify optimal morphologies. In this work, we propose a novel morphology optimization method driven by a classifier-guided diffusion model to enhance the search efficiency of EAs. Prior to the design process, a universal diffusion model is trained using a set of randomly sampled feasible morphologies, prompting that the generated structures satisfy physical constraints. In each iteration of the EA for morphology optimization, a classifier is trained using previously evaluated morphologies and is then used to condition the diffusion model to generate quality solutions. Subsequently, the generated morphology is further refined based on the voxel distribution to incorporate features from the current high-quality morphology. Extensive experiments on a large-scale benchmark for co-designing the morphology and control of voxel-based soft robots demonstrate that our method significantly improves search efficiency in the morphological design space, outperforming both traditional EAs and surrogate-assisted EAs.