# Safe-VLN: Collision Avoidance for Vision-and-Language Navigation of Autonomous Robots Operating in Continuous Environments

arXiv: https://arxiv.org/abs/2311.02817 · HTML: https://mlsanity.com/paper/2311.02817/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2023.

## Built on by (most cited first, 22 of 22)

- [A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges](https://mlsanity.com/paper/2501.02189/) (2025)
- [UAV-ON: A Benchmark for Open-World Object Goal Navigation with Aerial Agents](https://mlsanity.com/paper/2508.00288/) (2025)
- [Towards Physically Executable 3D Gaussian for Embodied Navigation](https://mlsanity.com/paper/2510.21307/) (2025)
- [Vision-Language-Action Safety: Threats, Challenges, Evaluations, and Mechanisms](https://mlsanity.com/paper/2604.23775/) (2026)
- [Navigating Beyond Instructions: Vision-and-Language Navigation in Obstructed Environments](https://mlsanity.com/paper/2407.21452/) (2024)
- [CogDDN: A Cognitive Demand-Driven Navigation with Decision Optimization and Dual-Process Thinking](https://mlsanity.com/paper/2507.11334/) (2025)
- [PanoGen++: Domain-Adapted Text-Guided Panoramic Environment Generation for Vision-and-Language Navigation](https://mlsanity.com/paper/2503.09938/) (2025)
- [View Invariant Learning for Vision-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2507.08831/) (2025)
- [Endowing Embodied Agents with Spatial Reasoning Capabilities for Vision-and-Language Navigation](https://mlsanity.com/paper/2504.08806/) (2025)
- [CARE: Enhancing Safety of Visual Navigation through Collision Avoidance via Repulsive Estimation](https://mlsanity.com/paper/2506.03834/) (2025)
- [UNeMo: Collaborative Visual-Language Reasoning and Navigation via a Multimodal World Model](https://mlsanity.com/paper/2511.18845/) (2025)
- [Semantic Audio-Visual Navigation in Continuous Environments](https://mlsanity.com/paper/2603.19660/) (2026)
- [ASMA: An Adaptive Safety Margin Algorithm for Vision-Language Drone Navigation via Scene-Aware Control Barrier Functions](https://mlsanity.com/paper/2409.10283/) (2024)
- [AdaVLN: Towards Visual Language Navigation in Continuous Indoor Environments with Moving Humans](https://mlsanity.com/paper/2411.18539/) (2024)
- [Dynamic Topology Awareness: Breaking the Granularity Rigidity in Vision-Language Navigation](https://mlsanity.com/paper/2601.21751/) (2026)
- [LightZeroNav: Zero-Shot Vision Language Navigation in Continuous Environments Based on Lightweight VLMs](https://mlsanity.com/paper/2603.16947/) (2026)
- [Hierarchical Semantic-Augmented Navigation: Optimal Transport and Graph-Driven Reasoning for Vision-Language Navigation](https://mlsanity.com/paper/2606.01565/) (2026)
- [Collision-Aware Object-Goal Visual Navigation via Two-Stage Deep Reinforcement Learning](https://mlsanity.com/paper/2502.13498/) (2025)
- [Curriculum-Based Multi-Tier Semantic Exploration via Deep Reinforcement Learning](https://mlsanity.com/paper/2509.09356/) (2025)
- [Rule-VLN: Bridging Perception and Compliance via Semantic Reasoning and Geometric Rectification](https://mlsanity.com/paper/2604.16993/) (2026)
- [LCGNav: Local Candidate-Aware Geometric Enhancement for General Topological Planning in Vision-Language Navigation](https://mlsanity.com/paper/2605.09053/) (2026)
- [Security of Foundation-Model-Powered Embodied Agents: Attack Surfaces, Attacks, Defenses, and Evaluation](https://mlsanity.com/paper/2608.16843/) (2026)

## Abstract (arXiv)

The task of vision-and-language navigation in continuous environments (VLN-CE) aims at training an autonomous agent to perform low-level actions to navigate through 3D continuous surroundings using visual observations and language instructions. The significant potential of VLN-CE for mobile robots has been demonstrated across a large number of studies. However, most existing works in VLN-CE focus primarily on transferring the standard discrete vision-and-language navigation (VLN) methods to continuous environments, overlooking the problem of collisions. Such oversight often results in the agent deviating from the planned path or, in severe instances, the agent being trapped in obstacle areas and failing the navigational task. To address the above-mentioned issues, this paper investigates various collision scenarios within VLN-CE and proposes a classification method to predicate the underlying causes of collisions. Furthermore, a new VLN-CE algorithm, named Safe-VLN, is proposed to bolster collision avoidance capabilities including two key components, i.e., a waypoint predictor and a navigator. In particular, the waypoint predictor leverages a simulated 2D LiDAR occupancy mask to prevent the predicted waypoints from being situated in obstacle-ridden areas. The navigator, on the other hand, employs the strategy of `re-selection after collision' to prevent the robot agent from becoming ensnared in a cycle of perpetual collisions. The proposed Safe-VLN is evaluated on the R2R-CE, the results of which demonstrate an enhanced navigational performance and a statistically significant reduction in collision incidences.