Safety of Embodied Navigation: A Survey

• URL: http://arxiv.org/abs/2508.05855v1
• Date: Thu, 07 Aug 2025 21:09:48 GMT
• Title: Safety of Embodied Navigation: A Survey
• Authors: Zixia Wang, Jia Hu, Ronghui Mu,
• Abstract summary: Embodied navigation requires an agent to perceive, interact with, and adapt to its environment while moving toward a specified target in unfamiliar settings.<n>The integration of embodied navigation into critical applications raises substantial safety concerns.<n>This survey provides a comprehensive analysis of safety in embodied navigation from multiple perspectives.
• Score: 7.093351303699698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As large language models (LLMs) continue to advance and gain influence, the development of embodied AI has accelerated, drawing significant attention, particularly in navigation scenarios. Embodied navigation requires an agent to perceive, interact with, and adapt to its environment while moving toward a specified target in unfamiliar settings. However, the integration of embodied navigation into critical applications raises substantial safety concerns. Given their deployment in dynamic, real-world environments, ensuring the safety of such systems is critical. This survey provides a comprehensive analysis of safety in embodied navigation from multiple perspectives, encompassing attack strategies, defense mechanisms, and evaluation methodologies. Beyond conducting a comprehensive examination of existing safety challenges, mitigation technologies, and various datasets and metrics that assess effectiveness and robustness, we explore unresolved issues and future research directions in embodied navigation safety. These include potential attack methods, mitigation strategies, more reliable evaluation techniques, and the implementation of verification frameworks. By addressing these critical gaps, this survey aims to provide valuable insights that can guide future research toward the development of safer and more reliable embodied navigation systems. Furthermore, the findings of this study have broader implications for enhancing societal safety and increasing industrial efficiency.

Related papers

• OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows [77.95511352806261]
  Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms.<n>We propose OS-Sentinel, a novel hybrid safety detection framework that combines a Formal Verifier for detecting explicit system-level violations with a Contextual Judge for assessing contextual risks and agent actions.
  arXiv  Detail & Related papers  (2025-10-28T13:22:39Z)
• Towards Trustworthy GUI Agents: A Survey [64.6445117343499]
  This survey examines the trustworthiness of GUI agents in five critical dimensions.<n>We identify major challenges such as vulnerability to adversarial attacks, cascading failure modes in sequential decision-making.<n>As GUI agents become more widespread, establishing robust safety standards and responsible development practices is essential.
  arXiv  Detail & Related papers  (2025-03-30T13:26:00Z)
• AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement [73.0700818105842]
  We introduce AISafetyLab, a unified framework and toolkit that integrates representative attack, defense, and evaluation methodologies for AI safety.<n> AISafetyLab features an intuitive interface that enables developers to seamlessly apply various techniques.<n>We conduct empirical studies on Vicuna, analyzing different attack and defense strategies to provide valuable insights into their comparative effectiveness.
  arXiv  Detail & Related papers  (2025-02-24T02:11:52Z)
• Navigating Threats: A Survey of Physical Adversarial Attacks on LiDAR Perception Systems in Autonomous Vehicles [4.4538254463902645]
  LiDAR systems are vulnerable to adversarial attacks, which pose significant challenges to the safety and robustness of autonomous vehicles. This survey presents a review of the current research landscape on physical adversarial attacks targeting LiDAR-based perception systems. We identify critical challenges and highlight gaps in existing attacks for LiDAR-based systems.
  arXiv  Detail & Related papers  (2024-09-30T15:50:36Z)
• Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? [59.96471873997733]
  We propose an empirical foundation for developing more meaningful safety metrics and define AI safety in a machine learning research context.<n>We aim to provide a more rigorous framework for AI safety research, advancing the science of safety evaluations and clarifying the path towards measurable progress.
  arXiv  Detail & Related papers  (2024-07-31T17:59:24Z)
• Sok: Comprehensive Security Overview, Challenges, and Future Directions of Voice-Controlled Systems [10.86045604075024]
  The integration of Voice Control Systems into smart devices accentuates the importance of their security. Current research has uncovered numerous vulnerabilities in VCS, presenting significant risks to user privacy and security. This study introduces a hierarchical model structure for VCS, providing a novel lens for categorizing and analyzing existing literature in a systematic manner. We classify attacks based on their technical principles and thoroughly evaluate various attributes, such as their methods, targets, vectors, and behaviors.
  arXiv  Detail & Related papers  (2024-05-27T12:18:46Z)
• The Last Decade in Review: Tracing the Evolution of Safety Assurance Cases through a Comprehensive Bibliometric Analysis [7.431812376079826]
  Safety assurance is of paramount importance across various domains, including automotive, aerospace, and nuclear energy. The use of safety assurance cases allows for verifying the correctness of the created systems capabilities, preventing system failure.
  arXiv  Detail & Related papers  (2023-11-13T17:34:23Z)
• Leveraging Traceability to Integrate Safety Analysis Artifacts into the Software Development Process [51.42800587382228]
  Safety assurance cases (SACs) can be challenging to maintain during system evolution. We propose a solution that leverages software traceability to connect relevant system artifacts to safety analysis models. We elicit design rationales for system changes to help safety stakeholders analyze the impact of system changes on safety.
  arXiv  Detail & Related papers  (2023-07-14T16:03:27Z)
• Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements [76.80453043969209]
  This survey presents a framework for safety research pertaining to large models. We begin by introducing safety issues of wide concern, then delve into safety evaluation methods for large models. We explore the strategies for enhancing large model safety from training to deployment.
  arXiv  Detail & Related papers  (2023-02-18T09:32:55Z)
• Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based Navigation [5.706538676509249]
  We propose a semantic Deep-reinforcement-learning-based navigation approach that teaches object-specific safety rules by considering high-level obstacle information. We demonstrate that the agent could learn to navigate more safely by keeping an individual safety distance dependent on the semantic information.
  arXiv  Detail & Related papers  (2021-09-23T10:50:47Z)

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.