To address this challenge, Nuoyin proposes a technical path from VLA to AIGA: shifting from “imitating what exists” to “generating what is needed.” By modeling a more complete action space and skill structure, and combining that with large-scale synthetic-data training, robots can, under the constraints of real-time environmental state and long-horizon task objectives, autonomously generate action sequences and execution strategies better suited to the current context. With less reliance on real demonstration data, they can gradually develop transferable, composable new skills—significantly improving adaptability and practicality in home settings.
（十一）经谨慎处理仍不能发现的船舶潜在缺陷；
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