視覚ベースロボット制御のための統計的保証付き失敗予測
Failure Prediction with Statistical Guarantees for Vision-Based Robot Control
PAC-Bayes理論を用いて、視覚入力に基づくロボット制御の失敗を偽陽性・偽陰性の保証付きで予測する手法を提案し、ドローンのナビゲーションとロボットアームの把持で有効性を示した。
著者: Alec Farid, David Snyder, Allen Z. Ren, Anirudha Majumdar
分類: cs.RO
原文アブストラクト
We are motivated by the problem of performing failure prediction for safety-critical robotic systems with high-dimensional sensor observations (e.g., vision). Given access to a black-box control policy (e.g., in the form of a neural network) and a dataset of training environments, we present an approach for synthesizing a failure predictor with guaranteed bounds on false-positive and false-negative errors. In order to achieve this, we utilize techniques from Probably Approximately Correct (PAC)-Bayes generalization theory. In addition, we present novel class-conditional bounds that allow us to trade-off the relative rates of false-positive vs. false-negative errors. We propose algorithms that train failure predictors (that take as input the history of sensor observations) by minimizing our theoretical error bounds. We demonstrate the resulting approach using extensive simulation and hardware experiments for vision-based navigation with a drone and grasping objects with a robotic manipulator equipped with a wrist-mounted RGB-D camera. These experiments illustrate the ability of our approach to (1) provide strong bounds on failure prediction error rates (that closely match empirical error rates), and (2) improve safety by predicting failures.
