ニューラルネットワーク修復による支援機器の安全なロボット学習
Safe Robot Learning in Assistive Devices through Neural Network Repair
支援ロボットのNN制御方針を、形式的な安全制約を満たすように混合整数二次計画問題として修復する手法を提案し、下腿義足で有効性を示した。
著者: Keyvan Majd, Geoffrey Clark, Tanmay Khandait, Siyu Zhou, Sriram Sankaranarayanan, Georgios Fainekos, Heni Ben Amor
分類: cs.RO
原文アブストラクト
Assistive robotic devices are a particularly promising field of application for neural networks (NN) due to the need for personalization and hard-to-model human-machine interaction dynamics. However, NN based estimators and controllers may produce potentially unsafe outputs over previously unseen data points. In this paper, we introduce an algorithm for updating NN control policies to satisfy a given set of formal safety constraints, while also optimizing the original loss function. Given a set of mixed-integer linear constraints, we define the NN repair problem as a Mixed Integer Quadratic Program (MIQP). In extensive experiments, we demonstrate the efficacy of our repair method in generating safe policies for a lower-leg prosthesis.
