“Trial & Error” Driven Embodied Intelligence via Learning and Evolution

How does a newborn baby learn to walk? It stumbles and falls repeatedly, but accumulates experience through each “trial and error” and eventually masters balance and coordination. This innate human learning and evolution mechanism is being given to machines by Embodied Intelligence. In this article, we propose the “ABCDE” integration framework-AI (cognitive engine), Body (physical carrier), Control (control hub), Decision (decision-making synergy) to drive Embodied Intelligence. This framework not only enables machine have a “brain”, but also gives it a “body” and “embodied perception”, so that the machine can evolve cognitive ability through “trial and error” like a living organism. Trial-and-error learning is a key mechanism to break the “algorithmic black box”, just like the causal chain established when a baby taps a toy, the intelligent body establishes a predictive model of environmental feedback through active exploration. This dynamic cognitive evolution mechanism allows the system to autonomously evolve the boundaries of intelligence in unknown scenarios. This not only promotes the paradigm shift of machine intelligence from disembodied computing to embodied operational practice, but also reveals the essence of intelligence: it originates from the correction of prediction errors when falling down, and becomes the optimization of action strategies when rising up.