Recent task-driven and just-in-time 3D Scene Graph (3DSG) methods reduce per-task representations by constructing or activating only task-relevant information. Yet sparse per-task working sets do not bound onboard memory usage over a robot's lifetime: as tasks change, payloads accumulated for earlier tasks may become irrelevant to the current task but can be useful again in future tasks. Over repeated task switches and expanding environments, retaining such reusable payloads causes local memory to grow, whereas discarding them entirely can lead to costly repeated construction of the same payloads later. We introduce PORTER, which decouples persistence from residency: lightweight anchors remain in the limited memory of the edge robot while heavy object payloads migrate between the edge and the cloud. Relevance alone is insufficient for deciding residency because multiple relevant payloads may provide redundant information. We therefore decompose each task into functional requirements and introduce Irreplaceable Support Erasure (ISE), which measures the loss in requirement coverage caused by offloading. ISE discounts replaceable support and penalizes losses more strongly when the remaining coverage of a requirement is weak. PORTER constructs a budget-aware local working set by repeatedly offloading the payload with the smallest marginal ISE per byte. Experiments on JITOMA-Bench evaluate PORTER across four 3DSG builders. Under progressive compression, pooled relative mR@3 remains at 100% through 91% payload-byte offloading.
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.
Yue Chang, Rufeng Chen, Yifan Tian +7
1The Hong Kong University of Science and Technology (Guangzhou) · 2Jilin University
Long-horizon robotic manipulation fundamentally relies on persistent spatial memory. However, existing 3D memory systems function merely as passive recorders: they store observations using fixed, hand-crafted rules, treating every scene element--whether a critical grasp target or an irrelevant background wall--with equal importance. In this paper, we propose a paradigm shift from passive storage to active, task-driven spatial memory. We argue that a robot's memory should not simply record what it sees, but actively learn how to remember--discovering which objects to track precisely, how aggressively to update them, and what to discard, all learned end-to-end without hand-designed rules. Crucially, this active paradigm is realized by unifying memory update and readout as two sides of the same cognitive process, enabling bidirectional flow where task needs shape update strategies and vice versa. To instantiate this vision, we introduce GaussMemory, which leverages 3D Gaussian Splatting as a persistent geometric substrate. On LIBERO, GaussMemory outperforms MemoryVLA on Goal and Long-10; on VLABench, it surpasses π0-FAST by +5.2% (Track 1) and +6.0% (Track 6).
Robotic agents use 3D scene graphs (3DSG) to perform tasks ranging from scene understanding to scene interaction. Although an extensive body of work addresses scene graph generation, little attention has been paid to optimizing the graph for consumption, which leaves state-of-the-art perception pipelines to work around their own scene graph and to pay a latency cost that does not fit the real-time budget a perception loop runs on. We present Yggdrasil, the first 3D scene graph designed to be efficient for both generation and consumption: a layer-first hierarchical graph built from generic nodes, edges, and layers, which expresses the representations existing pipelines already produce, indoor or outdoor, flat or hierarchical, while natively answering the positional and semantic queries downstream tasks issue. Against a published DSG baseline, Yggdrasil answers queries up to 121× faster, and every query we measure falls between 2 and 127 microseconds, three to five orders of magnitude inside the 200 microsecond keyframe budget a 3DSG consumer lives in, on both a workstation and embedded class device. We integrate Yggdrasil into three published pipelines spanning human trajectory prediction, object-goal navigation, and human-aware motion planning, where it removes up to 99% of the time each spends on its scene graph. The implementation, benchmark harness, and all three integrations are available online.
Department of Computer Science, San Diego State University · Division of Robotics, Perception and Learning, KTH Royal Institute of Technology · Department of Computer Science, University of Illinois Chicago