Just-In-Time Scene Graph Growth: Combating Perceptual Saturation in Long-Horizon Robotics
This paper introduces JITOMA (Just-In-Time On-demand Memory Activation), a closed-loop framework that unifies task reasoning, perception, and memory to combat perceptual saturation in long-horizon robotics. It uses a task heatmap to filter observations and an LLM to dynamically activate relevant anchors, reducing computational overhead while maintaining stable performance. The authors also present JITOMA-Bench for evaluation.
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[Submitted on 14 Jul 2026]
Title:Just-In-Time Scene Graph Growth: Combating Perceptual Saturation in Long-Horizon Robotics
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Abstract:While 3D Scene Graphs (3DSGs) provide crucial structured representations for embodied agents, conventional Ahead-of-Time, build-everything-then-filter pipelines conflict with the real-time, low-latency demands of edge platforms, inducing a perceptual saturation effect via severe observation redundancy. To resolve this, we present JITOMA (Just-In-Time On-demand Memory Activation), a closed-loop framework that unifies task reasoning, perception, and memory into a just-in-time growth process. Instead of exhaustively mapping the entire environment, JITOMA leverages a top-down task heatmap at the frontend to filter continuous observations, routing minimal streams to maintain a global foundation of low-cost, dormant anchors. Upon a cognitive query, the backend Large Language Model (LLM) parses the robotic intent to dynamically awaken task-relevant anchors, triggering resource-intensive operations -- such as dense node captioning and functional inference -- exclusively within the activated local subgraph. To evaluate these dynamic capabilities and study perceptual saturation trade-offs, we introduce JITOMA-Bench, a comprehensive suite for long-horizon multi-tasking and complex multi-step reasoning. Extensive experiments demonstrate that JITOMA substantially reduces active graph size and captioning latency, while maintaining stable processing time under long-horizon task switching.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
Cite as: arXiv:2607.13245 [cs.CV]
(or arXiv:2607.13245v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.13245
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yue Chang [view email] [v1] Tue, 14 Jul 2026 20:14:50 UTC (15,090 KB)
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