Conformal Predictionによるサンプル効率の良い安全保証
Sample-Efficient Safety Assurances using Conformal Prediction
Conformal Predictionとロボット・環境ダイナミクスのシミュレータを組み合わせ、1/ε程度の少ないデータで偽陰性率εを理論的に保証する警告システムを構築し、運転警告やロボット把持に適用した。
著者: Rachel Luo, Shengjia Zhao, Jonathan Kuck, Boris Ivanovic, Silvio Savarese, Edward Schmerling, Marco Pavone
分類: cs.RO, cs.LG
原文アブストラクト
When deploying machine learning models in high-stakes robotics applications, the ability to detect unsafe situations is crucial. Early warning systems can provide alerts when an unsafe situation is imminent (in the absence of corrective action). To reliably improve safety, these warning systems should have a provable false negative rate; i.e. of the situations that are unsafe, fewer than $\epsilon$ will occur without an alert. In this work, we present a framework that combines a statistical inference technique known as conformal prediction with a simulator of robot/environment dynamics, in order to tune warning systems to provably achieve an $\epsilon$ false negative rate using as few as $1/\epsilon$ data points. We apply our framework to a driver warning system and a robotic grasping application, and empirically demonstrate guaranteed false negative rate while also observing low false detection (positive) rate.
