PreSIST：开放世界场景中视觉-语言信息的对象持久性预测
PreSIST: Vision-Language-Informed Object Persistence Prediction in Open-World Scenes
AI总结：
研究长期部署机器人对环境变化的推理，提出PreSIST方法，通过对象属性和场景上下文估计实例级持久性先验，结合概率持久性过滤器预测对象未来姿态，有两种变体，实验表明优于基线。
摘要：
长期部署的机器人须对随时间变化的环境进行推理。现有系统常被动应对对象变化，机器人应主动利用对象出现的上下文推断其可能的持续时间。我们提出PreSIST，一种预测观察到的对象在任意未来时间是否会保持其最后看到姿态的方法。PreSIST从对象属性和场景上下文估计实例级持久性先验，并将这些先验与概率持久性过滤器集成。我们开发了两种可互换的变体：PreSIST-Lang和PreSIST-Vis。在新的野外对象持久性注释数据集上的实验表明，PreSIST-Lang和PreSIST-Vis在开放世界持久性预测方面优于基线。
英文摘要：
Robots deployed over long periods must reason about environments that change over time. Existing long-term perception systems often address object change reactively, updating their maps only after revisiting a scene and observing that an object has moved. Instead, robots should reason proactively about how long objects are likely to persist using the context in which they appear. For example, a car at a traffic light and a car in a parking spot share the same semantic class, but their contexts imply different persistence durations. We propose PreSIST (Predictive Scene-conditioned Instance Survival over Time), a method for predicting whether an observed object will remain in its last seen pose at arbitrary future times. PreSIST estimates instance-level persistence priors from object properties and scene context, then integrates these priors with a probabilistic persistence filter as observations become available. Its key insight is that the reasoning capabilities of vision-language models (VLMs) can relate scene context to likely object use and human activity, enabling persistence prediction before long-term observations are available. We develop two interchangeable variants: PreSIST-Lang, which estimates persistence priors using a VLM, and PreSIST-Vis, a novel vision-only model trained using PreSIST-Lang pseudo-labels for efficient deployment. Experiments on a new dataset of in-the-wild object persistence annotations show that PreSIST-Lang and PreSIST-Vis outperform baselines on open-world persistence prediction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
