Given the complexity of home scenarios and their long-tail distribution, today’s mainstream technical approaches are still evolving. On the data side, training data often relies on lab demonstrations, limited real-world trajectories, and publicly available videos, leaving significant room to improve generalization to unknown environments and novel task combinations. On the objective and representation side, traditional VLA systems are typically optimized around aligning vision–language–action and reproducing behaviors; deeper modeling of the semantic structure behind actions and a composable skill space is still needed. As a result, models behave more like they are “matching/reusing” existing action fragments rather than generating feasible new strategies based on goals and constraints, making it difficult to handle the highly long-tailed and constantly changing task demands found in real homes.
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模型已在中央气象台及陕西、宁夏等10余省（自治区）气象部门业务运行，并通过中国气象局天气预报科技成果中试基地业务准入，在沙尘天气过程预报复盘、环境气象业务会商中发挥了关键支撑作用。