让生成模型生成的内容100%满足规划要求，无需重新训练。
Certified Guidance for Planning with Deep Generative Models
- 通过神经网络验证技术，锁定生成模型中保证满足逻辑约束的潜在空间。
- 在四个规划基准上测试，生成结果100%符合信号时序逻辑要求。
- 适合对安全性和可靠性要求极高的自主系统规划任务。
深度生成模型（如生成对抗网络和扩散模型）已成为自主系统规划与行为合成的强大工具。现有引导策略可调整生成过程以逼近规划目标，但无法保证输出一定满足需求。为此，本文提出认证引导（certified guidance），在不重训练模型的前提下，将生成模型改造为能以概率1满足指定规范的模型。研究聚焦于信号时序逻辑（STL），其表达能力足以描述复杂规划任务。方法利用神经网络验证技术，系统探索生成模型的潜在空间，识别出对特定STL性质具有认证正确性的潜在区域。在使用GAN和扩散模型的四个规划基准上进行了评估，结果表明，认证引导生成的模型始终满足要求，而现有非认证引导方法则无法保证。
原文摘要 · Abstract (English)
Deep generative models, such as generative adversarial networks and diffusion models, have recently emerged as powerful tools for planning tasks and behavior synthesis in autonomous systems. Various guidance strategies have been introduced to steer the generative process toward outputs that are more likely to satisfy the planning objectives. These strategies avoid the need for model retraining but do not provide any guarantee that the generated outputs will satisfy the desired planning objectives. To address this limitation, we introduce certified guidance, an approach that modifies a generative model, without retraining it, into a new model guaranteed to satisfy a given specification with probability one. We focus on Signal Temporal Logic specifications, which are rich enough to describe nontrivial planning tasks. Our approach leverages neural network verification techniques to systematically explore the latent spaces of the generative models, identifying latent regions that are certifiably correct with respect to the STL property of interest. We evaluate the effectiveness of our method on four planning benchmarks using GANs and diffusion models. Our results confirm that certified guidance produces generative models that are always correct, unlike existing guidance methods that are not certified.
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