这篇论文把机器人闪存写寿命当成钱来算，发现只有便宜芯片上才需要省着用，高端芯片随便写。读它学怎么用价格信号管好机器人记忆。
该研究将机器人闪存耐久性视为非可再生资源，提出用单个影子价格η优化数据在RAM、板载NVM和云之间的分布。在重复长程操作场景中，价值-写入关联χ的测量值约为+1.0×10^{-3}，短程场景中接近零，非重复遥操作场景中为负。高端TLC闪存（3000 P/E）上耐久预算不构成约束，但廉价QLC/eMMC（约1000 P/E）上具有约束性。学习型磨损感知控制器在任务价值上与基于价格的路由持平，因为实现的价值在不同层级间保持不变。非单调最优已被证明但尚未在实验数据中观察到。
Memory as a Wasting Asset: Pricing Flash Endurance for Embodied Agents, and the Limits of Doing So
A robot's flash endurance is a non-renewable stock: every persisted write spends one of a few thousand program/erase cycles and never refills, yet no fielded robot memory system prices which memories are worth an erase cycle. We treat embodied memory as depreciating capital and price that stock with a single endurance shadow price $η$, which makes cost-minimizing placement across a RAM / on-board NVM / cloud hierarchy a threshold in a wear-augmented per-byte index. The index is cost-optimal whatever the sign of the value-write association $χ$; only when $χ> 0$ does the optimum turn non-monotone, sending a robot's most valuable memories off its flash. The pivot is thus empirical, and we measure $χ$ on real robot logs at a pre-specified gate: its sign is a property of the deployment regime -- positive on recurrent long-horizon manipulation ($\hatχ \approx +1.0 \times 10^{-3}$, replicated at full power), null on a shorter-horizon suite, and negative on non-recurrent teleoperation. Two boundaries scope the result. The endurance budget is dormant on premium 3,000-P/E TLC at datasheet prices and binding on the commodity QLC/eMMC ($\sim$1,000 P/E) that cheaper edge robots run. And where it binds, a learned wear-aware controller only ties price-based routing on task value, because realized value is tier-invariant across RAM, NVM, and cloud: the rent governs device lifetime and cost, not task performance. Whether wear-aware placement improves task value remains open -- $χ$ is measured against a value proxy, and the non-monotone optimum, while proven, is not yet observed in data.