用图像轨迹学习潜空间，无需状态信息即可估计安全区域。
V-MORALS: Visual Morse Graph-Aided Estimation of Regions of Attraction in a Learned Latent Space
- 基于图像轨迹学习低维潜空间，构建视觉莫尔斯图
- 仅需传感器数据即可计算多种系统与控制器的安全吸引域
- 适合无完整状态观测的机器人安全分析场景
可达性分析在机器人领域对区分安全与不安全状态至关重要。现有方法通常依赖已知系统动力学或大量数据来建模，计算成本高且要求全状态信息。近期的MORALS方法通过拓扑工具在低维潜空间中估计吸引域（ROA），但仍需完整状态。本文提出视觉莫尔斯图辅助的潜空间可达性估计方法（V-MORALS），仅需控制器下的图像轨迹数据，即可学习潜空间并生成清晰的莫尔斯图，进而计算不同系统与控制器的吸引域。该方法无需状态知识，仅依赖高阶传感器数据，具备与原始MORALS相当的能力。项目网站：https://v-morals.onrender.com。
原文摘要 · Abstract (English)
Reachability analysis has become increasingly important in robotics to distinguish safe from unsafe states. Unfortunately, existing reachability and safety analysis methods often fall short, as they typically require known system dynamics or large datasets to estimate accurate system models, are computationally expensive, and assume full state information. A recent method, called MORALS, aims to address these shortcomings by using topological tools to estimate Regions of Attraction (ROA) in a low-dimensional latent space. However, MORALS still relies on full state knowledge and has not been studied when only sensor measurements are available. This paper presents Visual Morse Graph-Aided Estimation of Regions of Attraction in a Learned Latent Space (V-MORALS). V-MORALS takes in a dataset of image-based trajectories of a system under a given controller, and learns a latent space for reachability analysis. Using this learned latent space, our method is able to generate well-defined Morse Graphs, from which we can compute ROAs for various systems and controllers. V-MORALS provides capabilities similar to the original MORALS architecture without relying on state knowledge, and using only high-level sensor data. Our project website is at: https://v-morals.onrender.com.
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