Kumaraditya Gupta

Predictive Spatio-Temporal Scene Graphs for Semi-Static Scenes

Miguel Saavedra-Ruiz

Shima Shahfar

Steven Parkison

We have seen tremendous recent progress in our ability to build "spatio-semantic" representations that enable robots to perform complex reas… (voir plus)oning 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

2026-04-29

arXiv (prépublication)

doi.org

arxiv.org

Object-Centric Agentic Robot Policies

Executing open-ended natural language queries in previously unseen environments is a core problem in robotics. While recent advances in imit… (voir plus)ation learning and vision-language modeling have enabled promising end-to-end policies, these models struggle when faced with complex instructions and new scenes. Their short input context also limits their ability to solve tasks over larger spatial horizons. In this work, we introduce OCARP, a modular agentic robot policy that executes user queries by using a library of tools on a dynamic inventory of objects. The agent builds the inventory by grounding query-relevant objects using a rich 3D map representation that includes open-vocabulary descriptors and 3D affordances. By combining the flexible reasoning abilities of an agent with a general spatial representation, OCARP can execute complex open-vocabulary queries in a zero-shot manner. We showcase how OCARP can be deployed in both tabletop and mobile settings due to the underlying scalable map representation.

2025-09-22

NeurIPS.cc/2025/Workshop/SpaVLE (poster)

openreview.net

OpenLex3D: A Tiered Evaluation Benchmark for Open-Vocabulary 3D Scene Representations

Christina Kassab

Sacha Morin

Martin Büchner

Matias Mattamala

Kumaraditya Gupta

Abhinav Valada

Liam Paull

Maurice Fallon

3D scene understanding has been transformed by open-vocabulary language models that enable interaction via natural language. However, at pre… (voir plus)sent the evaluation of these representations is limited to datasets with closed-set semantics that do not capture the richness of language. This work presents OpenLex3D, a dedicated benchmark for evaluating 3D open-vocabulary scene representations. OpenLex3D provides entirely new label annotations for scenes from Replica, ScanNet++, and HM3D, which capture real-world linguistic variability by introducing synonymical object categories and additional nuanced descriptions. Our label sets provide 13 times more labels per scene than the original datasets. By introducing an open-set 3D semantic segmentation task and an object retrieval task, we evaluate various existing 3D open-vocabulary methods on OpenLex3D, showcasing failure cases, and avenues for improvement. Our experiments provide insights on feature precision, segmentation, and downstream capabilities. The benchmark is publicly available at: https://openlex3d.github.io/.

2025-09-17

NeurIPS.cc/2025/Datasets_and_Benchmarks_Track (poster)

openreview.net