Certified Guidance for Planning with Deep Generative Models

2025-01-22

AI Summary

Deep generative models (e.g., GANs, diffusion models) lack formal correctness guarantees when deployed for planning in autonomous systems. Method: This paper introduces the “Certification-Guided” framework—the first approach enabling provably correct, STL (Signal Temporal Logic)-guided generation from pretrained models without retraining. It integrates neural network verification with latent-space geometric analysis to rigorously ensure that generated outputs satisfy given STL specifications with probability one. Results: Evaluated on four canonical planning benchmarks, the method achieves 100% logical constraint satisfaction while preserving generation quality—surpassing existing heuristic-based guidance techniques that lack theoretical correctness guarantees. This work establishes the first formally verifiable generative planning paradigm for trustworthy AI decision-making.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

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.

Problem

• Deep Generative Models
• Signal Temporal Logic
• Decision Making

Innovation

• Certifiable Guidance
• Deep Generative Models
• Signal Temporal Logic