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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.00121 |
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| _version_ | 1866909006895775744 |
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| author | Saavedra-Ruiz, Miguel Gauthier, Charlie Gupta, Kumaraditya Shahfar, Shima Ellis, Kirsty Parkison, Steven Paull, Liam |
| author_facet | Saavedra-Ruiz, Miguel Gauthier, Charlie Gupta, Kumaraditya Shahfar, Shima Ellis, Kirsty Parkison, Steven Paull, Liam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00121 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Predictive Spatio-Temporal Scene Graphs for Semi-Static Scenes Saavedra-Ruiz, Miguel Gauthier, Charlie Gupta, Kumaraditya Shahfar, Shima Ellis, Kirsty Parkison, Steven Paull, Liam Robotics 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. |
| title | Predictive Spatio-Temporal Scene Graphs for Semi-Static Scenes |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.00121 |