arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2407.21144eess.SYcs.SY
收藏

信号时序逻辑规范下少样本在线适应的多任务学习

Multi-Task Learning for Few-Shot Online Adaptation under Signal Temporal Logic Specifications

Andres Arias, Chuangchuang Sun

AI总结:

本文研究了一种考虑信号时序逻辑(STL)的基于多任务学习的控制方法,通过鲁棒度衡量任务合规性,利用序列凸规划处理非凸项,在两个动力系统中实现了少样本在线适应且对高度扰动表现出鲁棒性。

摘要:

多任务学习(MTL)旨在利用相关任务中包含的有用信息,以提升学习特定任务的泛化性能。作为一个有前景的领域,本文研究了一种考虑信号时序逻辑(STL)的基于MTL的控制方法。任务合规性通过鲁棒度(RD)来衡量,该指标利用STL语义计算。本文提供了一种合适的方法来解决学习和测试阶段,对二次目标函数中的非凸项进行了适当处理,并使用了基于信赖域更新的序列凸规划。在学习阶段,从确定性目标生成任务集合,为测试阶段获得一个强初始化器,其中相关任务在更大扰动影响下被求解。该方法在两个动力系统中表现出鲁棒性,结果显示在测试阶段的少样本情形下即可满足任务规范,即使是对于高度扰动的任务。

英文摘要:

Multi-task learning (MTL) seeks to improve the generalized performance of learning specific tasks, exploiting useful information incorporated in related tasks. As a promising area, this paper studies an MTL-based control approach considering Signal Temporal Logic (STL). Task compliance is measured via the Robustness Degree (RD) which is computed by using the STL semantics. A suitable methodology is provided to solve the learning and testing stages, with an appropriate treatment of the non-convex terms in the quadratic objective function and using Sequential Convex Programming based on trust region update. In the learning stage, an ensemble of tasks is generated from deterministic goals to obtain a strong initializer for the testing stage, where related tasks are solved with a larger impact of perturbation. The methodology demonstrates to be robust in two dynamical systems showing results that meet the task specifications in a few shots for the testing stage, even for highly perturbed tasks.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑