Memory as a Wasting Asset: Pricing Flash Endurance for Embodied Agents, and the Limits of Doing So

This paper introduces a novel framework for pricing the flash endurance of embodied agents, treating memory as a depreciating asset with a single endurance shadow price ($\eta$). It presents a cost-minimizing memory placement strategy across RAM, on-board NVM, and cloud storage, revealing that the optimal memory allocation depends on the value-write association ($\chi$), which varies with the deployment context. The findings highlight the importance of wear-aware memory management in optimizing robot performance and costs, particularly for edge devices using lower-endurance flash types, although empirical validation of the proposed non-monotone optimum remains to be established.