In robotic systems, flash endurance is treated as a non-renewable asset: each write operation consumes a limited number of program/erase cycles and does not refill. Yet, no existing robotic memory system prices the value of flash memory. We conceptualize embodied memory as depreciating capital and price it with a single endurance shadow price $\eta$, enabling cost-minimizing placement across a RAM, on-board NVM, and cloud hierarchy as a threshold in a wear-augmented per-byte index. The index remains cost-optimal regardless of the sign of the value-write association $\chi$; it is only when $\chi > 0$ that the optimum becomes non-monotone, causing the robot's most valuable memories to shift away from flash. This pivot is empirical, and we measure $\chi$ using real robot logs at a predetermined gate: its sign is a characteristic of the deployment regime—positive during recurrent long-horizon manipulation ($\hat{\chi} \approx +1.0 \times 10^{-3}$, replicated at full power), null during shorter horizons, and negative in non-recurrent teleoperation. Two boundaries scope the result. The endurance budget remains dormant on premium 3000 P/E TLC at datasheet prices and becomes binding on commodity QLC/eMMC (approximately 1000 P/E) used by cheaper edge robots. Where it binds, a learned wear-aware controller only routes based on task value, as realized value is tier-invariant across RAM, NVM, and cloud: the rent governs device lifetime and cost, not task performance. Whether wear-aware placement enhances task value remains an open question—$\chi$ is measured against a value proxy, and the non-monotone optimum, while proven, has yet to be observed in data.
Blogger's Review: This article provides an insightful exploration of the economics of flash endurance in robotic applications, highlighting the intricate relationship between memory management and task value. While a theoretical framework has been proposed, validating these theories in practical applications remains a pressing challenge. Future research is anticipated to uncover the actual utility of wear-aware controllers.