Artificial intelligence (AI) systems have taken massive strides designing prose, artwork, game play, software, and proteins, but have yet to master the design of complex physical machines. Here we introduce an automatic optimization method that can design self-moving machines — robots — from scratch by tracing failures in their behavior back to errors or inefficiencies in particular parts of their physical structure. Because this method improves the robot in this way, it can optimize the interdependent parts of the robot much more rapidly than the current approach, in which the designer tries different robot designs in a trial and error fashion. This opens the way toward bespoke AI-driven design of robots for a wide range of tasks, rapidly and on demand.

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