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

arXiv: https://arxiv.org/abs/2607.15589 · HTML: https://mlsanity.com/paper/2607.15589/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 19 references that are in the map.
- First posted on arXiv 17 Jul 2026.

## Builds on (most cited first, 14 of 14)

- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution](https://mlsanity.com/paper/1801.04134/) (2018)
- [Air-Ground Collaboration for Language-Specified Missions in Unknown Environments](https://mlsanity.com/paper/2505.09108/) (2025)
- [VLingNav: Embodied Navigation with Adaptive Reasoning and Visual-Assisted Linguistic Memory](https://mlsanity.com/paper/2601.08665/) (2026)
- [ACCESS-AV: Adaptive Communication-Computation Codesign for Sustainable Autonomous Vehicle Localization in Smart Factories](https://mlsanity.com/paper/2507.20399/) (2025)
- [Data-Efficient Multi-Agent Spatial Planning with LLMs](https://mlsanity.com/paper/2502.18822/) (2025)
- [Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence](https://mlsanity.com/paper/2510.26915/) (2025)
- [Collaboration in Multi-Robot Systems: Taxonomy and Survey over Frameworks for Collaboration](https://mlsanity.com/paper/2603.23898/) (2026)
- [Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning](https://mlsanity.com/paper/2409.16967/) (2024)
- [From Building Blocks to Planning: Multi-Step Spatial Reasoning in LLMs with Reinforcement Learning](https://mlsanity.com/paper/2512.24532/) (2025)
- [CoCoPlan: Adaptive Coordination and Communication for Multi-robot Systems in Dynamic and Unknown Environments](https://mlsanity.com/paper/2601.10116/) (2026)
- [FreqCache: Accelerating Embodied VLN Models with Adaptive Frequency-Guided Token Caching](https://mlsanity.com/paper/2604.24391/) (2026)
- [Joint Communication and Computation Design for Mobile Embodied AI Network (MEAN)](https://mlsanity.com/paper/2605.14300/) (2026)
- [Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems](https://mlsanity.com/paper/2605.21723/) (2026)

## Built on by (most cited first, 2 of 2)

- [FloodReasonBench: Benchmarking VLM Reasoning Segmentation for Embodied Flood Response at the Edge](https://mlsanity.com/paper/2608.15410/) (2026)
- [FRESHLATENT: Channel-Aware Latent Adaptation for Resource-Constrained Embodied VLM Perception](https://mlsanity.com/paper/2609.30629/) (2026)

## Abstract (arXiv)

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.