PORTER: Edge-Cloud Residency for Persistent 3D Scene Graph Memory

2026-09-27

AI Summary

This study addresses the prohibitive storage overhead incurred by unbounded expansion or redundant reconstruction of 3D scene graph memory on robotic edge devices during frequent task switching. To mitigate this, we propose an edge-cloud collaborative architecture that decouples memory persistence from residency, retaining lightweight anchors at the edge while offloading heavy payloads to the cloud. Furthermore, we introduce the Irreplaceable Support Erasure (ISE) metric to quantify offloading-induced performance degradation and design a marginal ISE minimization algorithm for budget-aware intelligent residency decisions. Extensive evaluations on the JITOMA-Bench benchmark demonstrate that the proposed framework maintains 100% relative mR@3 performance even when offloading 91% of the data volume, significantly enhancing long-term memory efficiency for embodied agents.

Abstract

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.

Problem

• 3D Scene Graph
• Edge-Cloud Computing
• Memory Management
• Lifelong Robotics
• Data Offloading

Innovation

• 3D Scene Graph
• Edge-Cloud Computing
• Irreplaceable Support Erasure
• Memory Management
• Persistent Anchors