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

Problem — This work addresses the lack of a systematic approach to pricing flash memory endurance in embodied agents, treating it as a depreciating asset. Existing memory systems do not account for the finite program/erase cycles of flash memory, leading to suboptimal memory management strategies. The authors highlight that this is a preprint and unreviewed work, indicating the need for further validation.

Method — The authors propose a model that assigns a single endurance shadow price (η) to the memory, allowing for cost-minimizing placement across a hierarchy of RAM, on-board non-volatile memory (NVM), and cloud storage. The model incorporates a wear-augmented per-byte index that optimally allocates memory based on the value-write association (χ). The empirical measurement of (χ) is derived from real robot logs, revealing its dependency on the deployment regime. The study identifies three distinct operational contexts: recurrent long-horizon manipulation (where (χ \approx +1.0 \times 10^{-3})), short-horizon tasks (where (χ) is null), and non-recurrent teleoperation (where (χ) is negative). The authors also discuss the implications of using premium versus commodity flash memory, noting that the endurance budget is more critical for lower-endurance QLC/eMMC memory.

Results — The proposed model demonstrates that the cost-optimal memory placement is contingent on the sign of (χ). When (χ > 0), the optimal placement becomes non-monotone, suggesting that the most valuable memories may be offloaded from the robot's flash storage. The empirical results indicate that the endurance budget is dormant for high-end 3,000 P/E TLC memory but binding for commodity QLC/eMMC, which is typically used in cost-sensitive edge robots. The learned wear-aware controller aligns memory routing with task value, indicating that the economic factors governing device lifetime and cost supersede task performance metrics.

Limitations — The authors acknowledge that while the non-monotone optimum is theoretically proven, it has not yet been observed in empirical data. Additionally, the measurement of (χ) relies on a value proxy, which may not fully capture the complexities of task performance. The study does not explore the potential for wear-aware placement to enhance task value, leaving this as an open question for future research.

Why it matters — This work has significant implications for the design of memory systems in embodied agents, particularly in optimizing resource allocation in environments with constrained flash endurance. By framing memory as a depreciating asset, the findings encourage a reevaluation of how memory resources are managed in robotic systems, potentially leading to more efficient and cost-effective designs. This research contributes to the broader discourse on resource management in AI systems, as published in arXiv cs.AI.