Industry Monitor Humanoid Industrial & Cobot AGV / AMR Quadruped Reducers · Servos · Sensors Drones & Autonomy Embodied AI
Robos News
Robotics

MemoGuard: An Adaptive Runtime for Guarding Against Memory Traps in Communication-Limited Robot Navigation

arXiv:2607.15589v1 Announce Type: new Abstract: Communication-limited robots in mission-critical scenarios such as disaster inspection and search-and-rescue must make reliable onboard decisions without access to remote operators or high-capacity reasoning services. Episodic memory reuse is an attractive low-cost fallback, but retrieval similarity does not guarantee execution validity, i.e., a retrieved action may match the current context yet be unsafe due to changed topology, insufficient batt

Published July 20, 2026 · Category: Robotics

Overview

arXiv:2607.15589v1 Announce Type: new Abstract: Communication-limited robots in mission-critical scenarios such as disaster inspection and search-and-rescue must make reliable onboard decisions without access to remote operators or high-capacity reasoning services. Episodic memory reuse is an attractive low-cost fallback, but retrieval similarity does not guarantee execution validity, i.e., a retrieved action may match the current context yet be unsafe due to changed topology, insufficient battery margin, or unreliable prior outcomes. We call such high-similarity but execution-invalid episodes memory traps. This creates a safety-efficiency design space where similarity only reuse minimizes fallback cost but can be unsafe, while always invoking local reasoning improves safety at high computational and energy cost. This paper presents MemoGuard, a lightweight adaptive runtime that validates episodic memories against topology, resource, and outcome contracts before reuse, invoking fallback only when validation fails. In a graph-based corridor-inspection simulator, MemoGuard reduces battery safety violations by 76.6% over similarity-only top-1 reuse while reducing fallback calls by 21.4% over always reasoning. On an NVIDIA Jetson AGX Xavier with local llama3.2:3b fallback reasoning, this corresponds to 3.67 s and 36.97 J of avoided fallback-reasoning overhead per trial. We open-source MemoGuard at https://github.com/hetheiin/memoguard.

Source

Originally published at arxiv.org.

Related Articles

Robos News Newsroom

Robos News reports on robotics research, components, manufacturers, field deployments, and industrial automation worldwide. Tip our newsroom: [email protected]

Email the newsroom →
Reporting standard: Product specifications, deployment counts, and performance claims are attributed to their source. Safety-critical decisions should be based on the applicable technical documentation and validation for the operating environment.
More from News →