Core Insights - The article discusses the limitations of current AI systems, particularly their inability to execute tasks in the physical world despite strong logical reasoning capabilities, a phenomenon termed "physical illusion" [4][11] - A new framework called "WorldMind" is introduced, which allows AI to learn from execution errors and successful experiences, enhancing their ability to interact with the physical environment [5][10] Group 1: Physical Illusion in AI - AI systems often generate plans that are logically sound but physically unexecutable, highlighting a disconnect between semantic reasoning and physical world understanding [4][11] - This issue is likened to a novice cook who can describe cooking steps but fails to perform them correctly due to a lack of practical experience [4] Group 2: WorldMind Framework - WorldMind is based on the predictive coding theory from cognitive science, emphasizing active prediction and error correction as a learning process [5] - The framework consists of two main components: process experience, which learns from execution errors, and goal experience, which extracts effective strategies from successful task completions [5][6] Group 3: Testing and Results - The effectiveness of WorldMind was validated in two testing environments, EB-ALFRED and EB-Habitat, showing significant improvements in task success rates, with EB-ALFRED increasing from 44.4% to 48.0% and EB-Habitat from 43.6% to 48.8% [7] - WorldMind demonstrated cross-model transfer capabilities, allowing experiences learned by one AI model to assist different AI models, thus promoting shared learning [7][10] Group 4: Broader Implications - The research suggests a shift towards AI systems that continuously learn and accumulate experiences rather than relying solely on static knowledge [9][10] - This approach aligns with human cognitive characteristics and may lead to more robust AI systems capable of adapting to dynamic environments [10]