# Predictive Spatio-Temporal Scene Graphs for Semi-Static Scenes

arXiv: https://arxiv.org/abs/2605.00121 · HTML: https://mlsanity.com/paper/2605.00121/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2026.

## Built on by (most cited first, 4 of 4)

- [PreSIST: Vision-Language-Informed Object Persistence Prediction in Open-World Scenes](https://mlsanity.com/paper/2607.04057/) (2026)
- [Robots That Take Initiative: A Framework for Building and Evaluating Proactive Robots](https://mlsanity.com/paper/2609.28910/) (2026)
- [Beyond the Remembered World: Predictive 4D Belief for Persistent Navigation in Evolving Worlds](https://mlsanity.com/paper/2609.39166/) (2026)
- [Retrospective Open-Vocabulary Memory for Long-Term Object Search](https://mlsanity.com/paper/2610.00330/) (2026)

## Abstract (arXiv)

We have seen tremendous recent progress in our ability to build "spatio-semantic" representations that enable robots to perform complex reasoning across geometry and semantics. However, the vast majority of these methods lack any ability to perform reasoning across time. This is a desirable property in situations where a robot repeatedly observes an environment where instances may change in between observations, but in a structured way. Consider as an example a home environment where the location of a mug typically moves from the cupboard to a countertop to the sink and then back to the cupboard on a daily basis. We should be able to learn this cyclic behavior and use it to predict the state of the mug in the future. In this work, we propose a method that is able to perform this type of tempo-spatio-semantic reasoning. Underpinning the method is a filter, Perpetua*, that performs Bayesian reasoning on the states of the environment that are observed over time. This filter is integrated within a 3D scene graph structure that we call PredictiveGraphs, where nodes represent objects and edges function as Perpetua* filters encoding spatio-semantic relationships. We validate the method in both simulation and real-world dynamic navigation tasks, where our real-world experiments consist of an environment that is undergoing semi-static changes at a bi-hourly frequency over a period of three weeks. In both settings, we demonstrate that our method outperforms baselines in predicting future environment states, even in the presence of distributional shifts.