Embodiment co-design aims to optimize a robot's morphology and control simultaneously, enhancing its environmental
adaptability. However, this task faces significant challenges due to its vast search space and bi-level optimization
nature. Existing approaches have used reinforcement learning to unify the optimization of morphology and control but
still face the drawback of inefficient message and reward delivery. To address this problem, we propose an end-to-end
reinforcement learning framework, Genesis, that optimizes message and reward delivery during co-design. Genesis utilizes
(1) a morphology self-attention architecture to achieve zero-delay message delivery during optimization, with our
proposed topology-aware positional encoding to achieve message localization and morphological knowledge sharing; (2) a
temporal credit assignment mechanism that ensures an agent receives balanced reward signals in both the morphology
design and control phases. Experiments across various tasks demonstrate Genesis's superiority over previous methods in
terms of both convergence speed and task performance.
