Pricing Flash Endurance for Embodied Agents as a Wasting Asset

Flash memory endurance in robots is a finite resource, with each write consuming one of a limited number of program/erase cycles. Current robot memory systems typically do not account for the cost or value associated with each erase cycle. This paper introduces a novel approach that models embodied memory as a depreciating capital asset. The core of this approach is an "endurance shadow price" ($\eta$), which allows for cost-optimized memory placement across different storage tiers: RAM, on-board non-volatile memory (NVM), and cloud storage. This pricing mechanism creates a wear-augmented per-byte index, guiding decisions on where to store data to minimize overall cost. The optimal placement strategy depends on the relationship between data value and write frequency, represented by the value-write association ($\chi$). When $\chi$ is positive, the most valuable memories might be offloaded from the robot's flash. Empirical measurements of $\chi$ using real robot logs show that its sign varies significantly with the robot's deployment context. For instance, it was positive for long-horizon manipulation tasks, null for shorter tasks, and negative for non-recurrent teleoperation. The study also identifies practical limits: premium flash (e.g., 3,000 P/E TLC) often has sufficient endurance, but commodity QLC/eMMC (around 1,000 P/E), common in more affordable edge robots, faces binding endurance budgets. In these constrained scenarios, a wear-aware controller primarily influences device lifetime and cost rather than directly improving task performance, as the realized value of data tends to be consistent across different memory tiers. The direct impact of wear-aware placement on task value remains an open question, as $\chi$ is currently measured against a proxy for value.