发表机构
FRANK Autonomous Systems Inc.; University of California, Berkeley（FRANK自主系统公司; 加州大学伯克利分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出507K参数循环架构FRANK，在100,000倍训练长度下保持100%准确率，优于50个基线配置，并成功驱动真实车辆导航。
AI 中文摘要
自主机器人在一次性任务中运行的时间跨度远超训练时看到的轨迹，且计算预算固定。我们提出了FRANK，一个507K参数的循环架构，结合了tau门控循环模块、内容可寻址存储和前馈反射通路。我们在四个算法序列任务上，以匹配的参数数量，将其与循环、状态空间和简化模块化基线进行比较，训练长度为5-20，评估至两百万个token。在最大训练长度的100,000倍处，10个FRANK种子中有6个保持精确100.0%的准确率，而50个基线配置中没有一个达到此水平，五种架构各十个种子均未完成（Fisher精确检验，双尾p = 4.2E-6）。针对四个任务的定向消融产生了四种不同的组件依赖模式，与任务依赖的循环、记忆和反射通路分配一致。另外，在模拟中训练的FRANK策略驱动物理地面车辆通过障碍物到达指定航点，无需远程操作。
英文摘要
Autonomous robots on one-shot missions run over horizons far longer than the trajectories seen during training, under a fixed onboard compute budget. We present FRANK, a 507K-parameter recurrent architecture that combines tau-gated recurrent modules, content-addressable memory, and a feedforward reflex pathway. We evaluate it against recurrent, state-space, and reduced modular baselines at matched parameter count on four algorithmic sequence tasks, trained at length 5-20 and evaluated out to two million tokens. At 100,000x the maximum training length, 6 of 10 FRANK seeds retain exactly 100.0% accuracy, while none of the 50 baseline configurations does, five architectures at ten seeds each with none left incomplete (Fisher exact, two-sided p = 4.2E-6. Targeted lesions across the four tasks yield four distinct component-reliance profiles, consistent with task-dependent allocation across the recurrent, memory, and reflex pathways. Separately, a FRANK policy trained in simulation drives a physical ground vehicle to commanded waypoints through obstacles without teleoperation.
Comments4 pages, 5 figures
