Certified Guidance for Planning with Deep Generative Models

• URL: http://arxiv.org/abs/2501.12815v1
• Date: Wed, 22 Jan 2025 11:46:28 GMT
• Title: Certified Guidance for Planning with Deep Generative Models
• Authors: Francesco Giacomarra, Mehran Hosseini, Nicola Paoletti, Francesca Cairoli,
• Abstract summary: Various guidance strategies have been introduced to steer the generative process toward outputs that are more likely to satisfy the planning objectives. 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. Our results confirm that certified guidance produces generative models that are always correct, unlike existing guidance methods that are not certified.
• Score: 1.391198481393699
• License:
• 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.

Related papers

• Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models [31.509112804985133]
  Reinforcement learning (RL) learns policies through trial and error, and optimal control, which plans actions using a learned or known dynamics model. We systematically analyze the performance of different RL and control-based methods under datasets of varying quality. Our results show that model-free RL excels when abundant, high-quality data is available, while model-based planning excels in generalization to novel environment layouts, trajectory stitching, and data-efficiency.
  arXiv  Detail & Related papers  (2025-02-20T18:39:41Z)
• Adaptive Planning with Generative Models under Uncertainty [20.922248169620783]
  Planning with generative models has emerged as an effective decision-making paradigm across a wide range of domains. While continuous replanning at each timestep might seem intuitive because it allows decisions to be made based on the most recent environmental observations, it results in substantial computational challenges. Our work addresses this challenge by introducing a simple adaptive planning policy that leverages the generative model's ability to predict long-horizon state trajectories.
  arXiv  Detail & Related papers  (2024-08-02T18:07:53Z)
• Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion Models [54.132297393662654]
  We introduce a hybrid method that fine-tunes cutting-edge diffusion models by optimizing reward models through RL. We demonstrate the capability of our approach to outperform the best designs in offline data, leveraging the extrapolation capabilities of reward models.
  arXiv  Detail & Related papers  (2024-05-30T03:57:29Z)
• LaPlaSS: Latent Space Planning for Stochastic Systems [8.529245639496274]
  We propose a "generate-and-test" approach to risk-bounded planning for autonomous mobile agents. We use a variational autoencoder to learn a latent linear dynamics model and encode the planning problem into the latent space to generate the candidate trajectory. We demonstrate that our algorithm, LaPlaSS, is able to generate trajectory plans with bounded risk for a real-world agent with learned dynamics.
  arXiv  Detail & Related papers  (2024-04-10T14:52:35Z)
• Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans [25.326624139426514]
  Diffusion-based planning has shown promising results in long-horizon, sparse-reward tasks. However, due to their nature as generative models, diffusion models are not guaranteed to generate feasible plans. We propose a novel approach to refine unreliable plans generated by diffusion models by providing refining guidance to error-prone plans.
  arXiv  Detail & Related papers  (2023-10-30T10:35:42Z)
• Learning Transferable Conceptual Prototypes for Interpretable Unsupervised Domain Adaptation [79.22678026708134]
  In this paper, we propose an inherently interpretable method, named Transferable Prototype Learning ( TCPL) To achieve this goal, we design a hierarchically prototypical module that transfers categorical basic concepts from the source domain to the target domain and learns domain-shared prototypes for explaining the underlying reasoning process. Comprehensive experiments show that the proposed method can not only provide effective and intuitive explanations but also outperform previous state-of-the-arts.
  arXiv  Detail & Related papers  (2023-10-12T06:36:41Z)
• Ownership Protection of Generative Adversarial Networks [9.355840335132124]
  Generative adversarial networks (GANs) have shown remarkable success in image synthesis. It is critical to technically protect the intellectual property of GANs. We propose a new ownership protection method based on the common characteristics of a target model and its stolen models.
  arXiv  Detail & Related papers  (2023-06-08T14:31:58Z)
• Towards Efficient Task-Driven Model Reprogramming with Foundation Models [52.411508216448716]
  Vision foundation models exhibit impressive power, benefiting from the extremely large model capacity and broad training data. However, in practice, downstream scenarios may only support a small model due to the limited computational resources or efficiency considerations. This brings a critical challenge for the real-world application of foundation models: one has to transfer the knowledge of a foundation model to the downstream task.
  arXiv  Detail & Related papers  (2023-04-05T07:28:33Z)
• Planning with Diffusion for Flexible Behavior Synthesis [125.24438991142573]
  We consider what it would look like to fold as much of the trajectory optimization pipeline as possible into the modeling problem. The core of our technical approach lies in a diffusion probabilistic model that plans by iteratively denoising trajectories.
  arXiv  Detail & Related papers  (2022-05-20T07:02:03Z)
• DST: Dynamic Substitute Training for Data-free Black-box Attack [79.61601742693713]
  We propose a novel dynamic substitute training attack method to encourage substitute model to learn better and faster from the target model. We introduce a task-driven graph-based structure information learning constrain to improve the quality of generated training data.
  arXiv  Detail & Related papers  (2022-04-03T02:29:11Z)
• Goal-Aware Prediction: Learning to Model What Matters [105.43098326577434]
  One of the fundamental challenges in using a learned forward dynamics model is the mismatch between the objective of the learned model and that of the downstream planner or policy. We propose to direct prediction towards task relevant information, enabling the model to be aware of the current task and encouraging it to only model relevant quantities of the state space. We find that our method more effectively models the relevant parts of the scene conditioned on the goal, and as a result outperforms standard task-agnostic dynamics models and model-free reinforcement learning.
  arXiv  Detail & Related papers  (2020-07-14T16:42:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.