Designing Latent Safety Filters using Pre-Trained Vision Models

• URL: http://arxiv.org/abs/2509.14758v1
• Date: Thu, 18 Sep 2025 09:06:37 GMT
• Title: Designing Latent Safety Filters using Pre-Trained Vision Models
• Authors: Ihab Tabbara, Yuxuan Yang, Ahmad Hamzeh, Maxwell Astafyev, Hussein Sibai,
• Abstract summary: Pre-trained vision models (PVRs) have been shown to be effective perception backbones for control in various robotics domains.<n>We use them as backbones for classifiers defining failure sets, for Hamilton-Jacobi (HJ) reachability-based safety filters, and for latent world models.<n>We discuss the trade-offs between training from scratch, fine-tuning, and freezing the PVRs when training the models they are backbones for.
• Score: 1.3085000473675192
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Ensuring safety of vision-based control systems remains a major challenge hindering their deployment in critical settings. Safety filters have gained increased interest as effective tools for ensuring the safety of classical control systems, but their applications in vision-based control settings have so far been limited. Pre-trained vision models (PVRs) have been shown to be effective perception backbones for control in various robotics domains. In this paper, we are interested in examining their effectiveness when used for designing vision-based safety filters. We use them as backbones for classifiers defining failure sets, for Hamilton-Jacobi (HJ) reachability-based safety filters, and for latent world models. We discuss the trade-offs between training from scratch, fine-tuning, and freezing the PVRs when training the models they are backbones for. We also evaluate whether one of the PVRs is superior across all tasks, evaluate whether learned world models or Q-functions are better for switching decisions to safe policies, and discuss practical considerations for deploying these PVRs on resource-constrained devices.

Related papers

• SafeVision: Efficient Image Guardrail with Robust Policy Adherence and Explainability [49.074914896839466]
  We introduce SafeVision, a novel image guardrail that integrates human-like reasoning to enhance adaptability and transparency.<n>Our approach incorporates an effective data collection and generation framework, a policy-following training pipeline, and a customized loss function.<n>We show that SafeVision achieves state-of-the-art performance on different benchmarks.
  arXiv  Detail & Related papers  (2025-10-28T00:35:59Z)
• Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation [55.02966123945644]
  We propose a hierarchical control framework leveraging neural network verification techniques to design control barrier functions (CBFs) and policy correction mechanisms.<n>Our approach relies on probabilistic enumeration to identify unsafe regions of operation, which are then used to construct a safe CBF-based control layer.<n>These experiments demonstrate the ability of the proposed solution to correct unsafe actions while preserving efficient navigation behavior.
  arXiv  Detail & Related papers  (2025-04-30T13:47:25Z)
• Learning Ensembles of Vision-based Safety Control Filters [0.0]
  Safety filters in control systems correct nominal controls that violate safety constraints.<n>Deep learning-based approaches to tackle this challenge have been proposed recently.<n>We empirically investigate the efficacy of ensembles in enhancing the accuracy and the out-of-distribution generalization of such filters.
  arXiv  Detail & Related papers  (2024-12-02T23:19:31Z)
• Pre-Trained Vision Models as Perception Backbones for Safety Filters in Autonomous Driving [2.4381063627159523]
  Safety remains a major concern in end-to-end vision-based autonomous driving. We use frozen pre-trained vision representation models as perception backbones to design vision-based safety filters. We empirically evaluate the offline performance of four common pre-trained vision models in this context.
  arXiv  Detail & Related papers  (2024-10-29T22:59:23Z)
• Modular Control Architecture for Safe Marine Navigation: Reinforcement Learning and Predictive Safety Filters [0.0]
  Reinforcement learning is increasingly used to adapt to complex scenarios, but standard frameworks ensuring safety and stability are lacking. Predictive Safety Filters (PSF) offer a promising solution, ensuring constraint satisfaction in learning-based control without explicit constraint handling. We apply this approach to marine navigation, combining RL with PSF on a simulated Cybership II model. Results demonstrate the PSF's effectiveness in maintaining safety without hindering the RL agent's learning rate and performance, evaluated against a standard RL agent without PSF.
  arXiv  Detail & Related papers  (2023-12-04T12:37:54Z)
• In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States [84.24300005271185]
  We propose a control filter that wraps any reference policy and effectively encourages the system to stay in-distribution with respect to offline-collected safe demonstrations. Our method is effective for two different visuomotor control tasks in simulation environments, including both top-down and egocentric view settings.
  arXiv  Detail & Related papers  (2023-01-27T22:28:19Z)
• Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks [70.76757529955577]
  This paper revisits prior work in this scope from the perspective of state-wise safe RL. We propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection. To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit.
  arXiv  Detail & Related papers  (2022-12-12T06:30:17Z)
• Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions [60.26921219698514]
  We introduce a model-uncertainty-aware reformulation of CBF-based safety-critical controllers. We then present the pointwise feasibility conditions of the resulting safety controller. We use these conditions to devise an event-triggered online data collection strategy.
  arXiv  Detail & Related papers  (2022-08-23T05:02:09Z)
• Safe RAN control: A Symbolic Reinforcement Learning Approach [62.997667081978825]
  We present a Symbolic Reinforcement Learning (SRL) based architecture for safety control of Radio Access Network (RAN) applications. We provide a purely automated procedure in which a user can specify high-level logical safety specifications for a given cellular network topology. We introduce a user interface (UI) developed to help a user set intent specifications to the system, and inspect the difference in agent proposed actions.
  arXiv  Detail & Related papers  (2021-06-03T16:45:40Z)
• Scalable Synthesis of Verified Controllers in Deep Reinforcement Learning [0.0]
  We propose an automated verification pipeline capable of synthesizing high-quality safety shields. Our key insight involves separating safety verification from neural controller, using pre-computed verified safety shields to constrain neural controller training. Experimental results over a range of realistic high-dimensional deep RL benchmarks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2021-04-20T19:30:29Z)

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.