脆弱な知覚に頑健な制御器の学習
Learning Certifiably Robust Controllers Using Fragile Perception
知覚誤差があっても安全に動作する制御器を、ガウス過程による状態推定とロバストなニューラルネットワーク制御器の合成、および適応的サンプリングで実現する手法を提案した。
著者: Dawei Sun, Negin Musavi, Geir Dullerud, Sanjay Shakkottai, Sayan Mitra
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Advances in computer vision and machine learning enable robots to perceive their surroundings in powerful new ways, but these perception modules have well-known fragilities. We consider the problem of synthesizing a safe controller that is robust despite perception errors. The proposed method constructs a state estimator based on Gaussian processes with input-dependent noises. This estimator computes a high-confidence set for the actual state given a perceived state. Then, a robust neural network controller is synthesized that can provably handle the state uncertainty. Furthermore, an adaptive sampling algorithm is proposed to jointly improve the estimator and controller. Simulation experiments, including a realistic vision-based lane-keeping example in CARLA, illustrate the promise of the proposed approach in synthesizing robust controllers with deep-learning-based perception.
