Searching in Space and Time

Unified Memory-Action Loops
for Open-World Object Retrieval

Taijing Chen, Sateesh Kumar, Junhong Xu, Georgios Pavlakos, Joydeep Biswas*, Roberto Martin-Martin*
Paper Code arXiv Benchmark (Coming Soon)
STAR teaser animation

Abstract

Service robots must retrieve objects in dynamic, open-world settings where requests may reference attributes (“the red mug”), spatial context (“the mug on the table”), or past states (“the mug that was here yesterday”). Existing approaches capture only parts of this problem: scene graphs capture spatial relations but ignore temporal grounding, temporal reasoning methods model dynamics but do not support embodied interaction, and dynamic scene graphs handle both but remain closed-world with fixed vocabularies. We present STAR (SpatioTemporal Active Retrieval), a framework that unifies memory queries and embodied actions within a single decision loop. STAR leverages non-parametric long-term memory and a working memory to support efficient recall, and uses a vision-language model to select either temporal or spatial actions at each step. We introduce STARBench, a benchmark of spatiotemporal object search tasks across simulated and real environments. Experiments in STARBench and on a Tiago robot show that STAR consistently outperforms scene-graph and memory-only baselines, demonstrating the benefits of treating search in time and search in space as a unified problem

STAR Overview

STAR in Real World

Three real-world examples of STAR in action — click arrows to explore.

Unmute videos for more information.

STAR on STARBench

STAR achieves the highest success rates across all task families — visible, interactive, and commonsense object search — demonstrating the benefit of unifying search in time and search in space.

More results and ablations are in the paper.

Visible Object Search in Simulation

Main STARBench results
STARBench: STAR substantially outperforms scene-graph and memory-only baselines on all spatiotemporal object retrieval tasks.

BibTeX

@misc{chen2025searchingspacetimeunified,
      title={Searching in Space and Time: Unified Memory-Action Loops for Open-World Object Retrieval}, 
      author={Taijing Chen and Sateesh Kumar and Junhong Xu and George Pavlakos and J oydeep Biswas and Roberto Martín-Martín},
      year={2025},
      eprint={2511.14004},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2511.14004}, 
}